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Record W4213045520 · doi:10.1139/cjps-2021-0276

AAC Excellence oat

2022· article· en· W4213045520 on OpenAlexaffvenueabout
Weikai Yan, Judith Fregeau-reid, Brad DeHaan, Steve Thomas, Matt Hayes, Richard A. Martin, Allan Cummiskey, Denis Pageau, Isabelle Morasse, Savka Orozovic, Jennifer Mitchell‐Fetch, Jim G. Menzies, Allen Xue, Nathan Mountain

Bibliographic record

VenueCanadian Journal of Plant Science · 2022
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsBrandon UniversityDefence Research and Development CanadaHealth PEIUniversity of Prince Edward IslandUniversity of GuelphAgriculture and Agri-Food Canada
Fundersnot available
KeywordsExcellenceYield (engineering)AvenaCultivarTest weightCenter of excellenceAgricultureAgronomyMathematicsHorticultureGeographyBiologyComputer sciencePolitical scienceDatabaseArchaeology

Abstract

fetched live from OpenAlex

AAC Excellence is a covered, spring oat (Avena sativa L.) cultivar developed by the Ottawa Research and Development Center (ORDC), Agriculture and Agri-Food Canada (AAFC). It was derived from a four-way cross, OA1250-1/MN07205//Rigodon/HiFi, made in 2009. It has been tested in the Quebec provincial Oat Registration and Recommendation (QCORR) trials since 2017. Based on orthogonal data from the 2018–2021 QCORR trials, AAC Excellence yielded 6% higher than the mean of official checks (AAC Dieter, Canmore, and CS Camden) and its yield was more stable across years than the checks and other cultivars. AAC Excellence had similar levels of test weight and kernel weight to those of the checks, a β-glucan level similar to Akina and better than AAC Nicolas and all checks, and a groat content level similar to AAC Nicolas and AC Dieter and better than Akina and other checks. It had a superior package of yield and quality and is most adapted to Quebec, the Maritimes, and northern Ontario.

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 score0.199
Threshold uncertainty score0.396

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1130.046

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 head0.023
GPT teacher head0.217
Teacher spread0.193 · 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

Citations2
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Plant Science→Same topicFood composition and properties→French-language works237,207→