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Record W6913023209 · doi:10.5683/sp3/qnndj1

ODRC data schema: Animal position

2024· dataset· en· W6913023209 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsRaw dataSchema (genetic algorithms)DocumentationData managementPosition (finance)Schema evolutionSoftwareHerd

Abstract

fetched live from OpenAlex

This is a data schema created using Agri-food Data Canada's Semantic Engine . Data schemas describe data that are collected on an ongoing basis from research centres, and provide add-on documentation that enhances the value of raw data. This schema is applicable to all datasets collecting daily animal position data at the barn extracted from SCR collars, DairyComp Herd Management Software's log files and Insentec VR files. Animal position from SCR is the last position on the day whereas DairyComp Herd Management Software shows all move events on a day or is implied to be the same as the last one informed until animal dies or leaves the herd. Positions from Insentec system is based on bin visits. Data described in this schema is sample data, to request access to the related data, please visit the Ontario Dairy Research Centre Data Portal.

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)0.006
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 score0.102
Threshold uncertainty score0.342

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.008
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1020.140

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 head0.048
GPT teacher head0.328
Teacher spread0.281 · 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
Published2024
Admission routes2
Has abstractyes

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