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Record W6970056936 · doi:10.5683/sp2/ovacvn

BC Seed Trials and CANOVI Replicated Carrot Variety Trials 2017-2019 - UBC Farm

2021· dataset· en· W6970056936 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2021
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicPolysaccharides and Plant Cell Walls
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsVariety (cybernetics)Quality (philosophy)Consistency (knowledge bases)Production (economics)Set (abstract data type)Missing data

Abstract

fetched live from OpenAlex

This dataset comprises data from three years of replicated carrot variety trials carried out at the UBC Farm. Production and quality data were collected as part of farmer-participatory variety trials that sought to identify carrot varieties with strong performance for both fresh market sales and seed production in British Columbia. Trials used the “mother-baby” design and included up to three “mother” sites (like UBC Farm) that conducted replicated trials, along with many on-farm “baby” sites that carried out unreplicated trials. The 2017 and 2018 trials were part of the BC Seed Trials project, and the 2019 trial was part of the nationwide Canadian Organic Vegetable Improvement (CANOVI) project. A different assortment of carrot varieties was trialed each year, and the set of traits evaluated also varied among years. This variability resulted in substantial missing data, but the dataset can be useful with appropriate filtering for varieties and traits of interest. The metadata tab summarizes the attributes rated per year, defines rating scales, and indicates when rating scales were converted for consistency among years.

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)0.002
metaresearch head score (Gemma)0.009
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.792
Threshold uncertainty score0.414

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0220.025

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 head0.075
GPT teacher head0.291
Teacher spread0.216 · 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
GenreDataset

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
Published2021
Admission routes2
Has abstractyes

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