1,720,988 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Optimizing soybean breeding with high-throughput digital phenotyping methods for relative maturity and yield prediction
Get PDFHigh-throughput phenotyping (HTP) is an emerging field providing plant breeding programs
with advanced tools for more efficient and precise data collection. UAV systems enable rapid field
scouting and can carry a range of sensors for versatile data collection, while ground-based robots
offer high-resolution imaging beneath the plant canopy, capturing details UAVs often miss.
Together, these HTP systems streamline breeding processes, conserving resources that would
otherwise be dedicated to manual tasks.
Accurately determining the maturity of soybean cultivars is essential for maximizing their yield
potential. We introduce an automated soybean relative maturity classification system using
convolutional neural networks (CNNs) with UAV imagery. This system extracts plot color change
over time to create a two-dimensional hue histogram, which is used to train a CNN model for
automated classification of soybean maturity. We also look into the number of UAV time points
needed to achieve accurate classifications. Additionally, we examine how the rate of greenness loss
relates to soybean maturity and yield, offering insights into potential additional selection criteria for
breeding.
While UAV systems offer a great tool for quick scouting of a breeding field, they often lack the
resolution needed to observe smaller plant features critical for yield estimation, such as soybean
pods and seeds. We present a HTP method for estimating soybean seed yield by using ground
robot video. This video-turned-imagery data is then used in conjunction with ML tools to detect
and quantify soybean seeds. This is then used to estimate and rank soybean plot yield.
The benefits of remote sensing and HTP techniques give breeding programs the ability to assign
critical labor and resources to other tasks. Unlike traditional large equipment, UAVs and ground
robots are generally easier to operate, requiring less specialized training and minimizing the
logistical challenges of transporting heavy machinery. UAVs offer the flexibility to cover large areas
quickly without the need for extensive setup, while ground robots can navigate fields and collect
detailed data without disturbing the plants. Maturity data collection is a highly laborious task and
can take multiple weeks to accomplish. Similarly, yield data collection requires specialized
equipment training and is logistically challenging. Utilizing ML tools with UAV and ground robot
systems can save countless labor hours and equipment breakdown costs
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Above-ground biomass and surface residue traits in soybean
Get PDFThe drive to increase seed yield in soybean [Glycine max (L.) Merr.] has traditionally
overshadowed the exploration of biomass partitioning and the compositional characteristics of
plant residue traits such as leaves, petioles, stems, and pods. Recognizing this gap, our study
aimed to investigate the variability in these traits across 32 genetically diverse soybean genotypes
cultivated over two years in central Iowa. Through detailed collection and analysis of vegetative
parts at critical growth stages (R1, R4, and R8), we assessed both biomass traits and their
compositional characteristics, focusing on soybean residue traits to enhance soil health and their
importance in soybean cropping systems. We present heritability estimates for biomass and
residue composition traits in soybean. The large variation and high heritability estimates suggest
avenues for breeding strategies to optimize variety development through improved biomass
partitioning and residue traits. Utilizing the Agriculture Production Systems Simulator (APSIM),
we conducted a sensitivity analysis to evaluate the impact of soybean residue quality on soil
nutrient cycling and its effects on the subsequent maize [Zea mays L.] crop. The study
underscores the importance of soybean residue management, emphasizing the need for integrated
approaches in breeding and agricultural practices that utilize the genetic diversity of these traits
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
The development and deployment of machine learning based high throughput phenotyping of soybean nodules and root system architecture traits for agronomic, physiological and genetic studies
Get PDFSoybeans [Glycine max L. (Merr.)] serve as a primary source of food, fuel, and commercial products around the world. As a legume they serve a critical role in crop production cycles where they are capable of fixing the majority of their required nitrogen endogenously. This nitrogen production occurs through the symbiotic relationship between soybeans and a soil born bacteria, most often Bradyrhizobium japonicum, in specialized root structures known as nodules. Within these nodules, diatomic atmospheric nitrogen (N2) is fixed into a plant bioavailable form of ammonia (NH3) which is used for vegetative growth, protein production, and seed fill. The roots these nodules grow on are also diverse and adaptive to their environments with variations in their physical structure known as root system architecture (RSA). The value of understanding the variation and adaptability of nodulation, RSA traits, and their interactions serves as a critical turning point to breeding and optimizing soybeans for specific environments and output traits. With recent developments in machine learning and computer vision this work presents the development of Soybean Nodule Acquisition Pipeline (SNAP), a novel tool, for evaluating nodulation which we re-define as the total nodule area on a root or root growth zone as a function of nodule count and individual nodule area. SNAP combines RetinaNet and Unet deep learning architectures to dramatically reduce the human labor needed to quantify nodule size and locations on roots. With the deployment of SNAP in early growth stages we show that nodule count does not statistically differ from V1 to V5 in the taproot growth zone, but nodulation continues to increase in every growth zone at each growth stage. We also found that in the panel assessed, the percent of nitrogen in end season seed has a moderate (r2 = 0.5) and significant (p = 0.04) correlation specifically to tap root nodulation. We also deployed SNAP on a large diversity panel (n=300) of soybeans in three field environments to conduct genome-wide association studies. As a result, we report three quantitative trait loci (QTL) across four traits and explore a putative gene for that may impact the Nodulation Carbon to Nitrogen Production Efficiency (NCNPE). Additionally, we explored the same diversity panel in controlled growth environments across the first 12 days of growth using the Advanced Root Image Analysis (ARIA) system for the underlying genetics of RSA and report 19 QTL across 21 traits in addition to finding five overlapping QTL with previously reported RSA traits. In these studies, we found no overlapping QTL between RSA and nodulation traits
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