MétaCan
Menu
Back to cohort
Record W4412902045 · doi:10.56367/oag-047-12094

Bean breeding at the University of Guelph: Past and present

2025· article· en· W4412902045 on OpenAlexaffabout
K. Peter Pauls, Mohsen Yoosefzadeh-Najafabadi

Bibliographic record

VenueOpen Access Government · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotanical Research and Chemistry
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

Bean breeding and at the University of Guelph: Past and present Examine bean breeding at the University of Guelph, both past and present, conducted by its Department of Plant Agriculture. The common dry bean (Phaseolus vulgaris) is a staple for >300 million people. Canadian bean production is worth >300M USD annually. Beans are highly nutritious, they contain nutraceuticals and have positive environmental effects through their ability to fix atmospheric nitrogen (Hesami and Yoosefzadeh-Najafabadi, 2025). Crop breeding and improved agronomic practices have resulted in 3.8 % per year yield increases in Ontario (Figure 1).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.547
Threshold uncertainty score0.901

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0390.008

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 head0.041
GPT teacher head0.299
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueOpen Access GovernmentSame topicBotanical Research and ChemistryFrench-language works237,207