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Record W1604803259 · doi:10.1159/000317192

Animal Genomics for Animal Health Report: Critical Needs, Problems to be Solved, Potential Solutions, and a Roadmap for Moving Forward

2008· book-chapter· en· W1604803259 on OpenAlexaff
Alan Archibald, J.-C. Audonnet, Lorne A. Babiuk, Steve Bishop, Cyril G. Gay, J. A. McKay, Bonnie A. Mallard, Graham Plastow, Marijke Laan, Montserrat Torremorell

Bibliographic record

VenueDevelopments in biologicals · 2008
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsUniversity of GuelphUniversity of Alberta
FundersBiotechnology and Biological Sciences Research Council
KeywordsAnimal healthGenomicsRound tablePerspective (graphical)Engineering ethicsOne HealthManagement scienceMedicineEngineeringComputer sciencePublic healthVeterinary medicineGenomeBiologyPathologySession (web analytics)

Abstract

fetched live from OpenAlex

The first International Symposium on Animal Genomics for Animal Health, held at the World Organisation for Animal Health (OIE) Headquarter, 23-25 October, 2007, Paris, France, assembled more than 250 participants representing research organizations from 26 countries. The symposium included a roundtable discussion on critical needs, challenges and opportunities, and a forward look at the potential applications of animal genomics in animal health research. The aim of the roundtable discussion was to foster a dialogue between scientists working at the cutting edge of animal genomics research and animal health scientists. In an effort to broaden the perspective of the roundtable discussion, the organizers set out four priority areas to advance the use of genome-enabled technologies in animal health research. Contributions were obtained through open discussions and a questionnaire distributed at the start of the symposium. This symposium report provides detailed summaries ofthe outcome ofthe roundtable discussion for each of the four priority areas. For each priority, the problems needing to be solved, according to the views of the participants, are identified, including potential solutions, recommendations, and lastly, concrete steps that could be taken to address these problems. This report serves as a roadmap to steer research priorities in animal genomics research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.054
GPT teacher head0.300
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2008
Admission routes1
Has abstractyes

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