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Record W112978473 · doi:10.21423/aabppro20074516

Managing Feeder Cattle with Confirmed Lead Exposure

2007· article· en· W112978473 on OpenAlexafffund
Sylvia Checkley, Clyde W. Jones, J. D. Kendall, Calvin W. Booker, Nickala Best, G. Kee Jim

Bibliographic record

VenueAmerican Association of Bovine Practitioners Conference Proceedings · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsAlberta Health ServicesAgriculture Food and Rural Development
FundersAgriculture and Agri-Food Canada
KeywordsLead (geology)FeedlotLead exposureAnimal scienceToxicologyMedicineBiotechnologyEnvironmental healthBiologyInternal medicine

Abstract

fetched live from OpenAlex

A commercial feedlot unknowingly fed lead contaminated feed to a portion of cattle in the feedyard. Due to the lack of data and regulations concerning how to manage the exposed animals, a proactive field study was developed to collect information that would help mitigate food safety and economic losses in this and other similar situations. After conducting an investigation into the scale of lead exposure in the feedlot, cattle found to have elevated blood lead concentrations were enrolled into the study. The objectives of this study were to collect data on blood and tissue lead concentrations over a number of months post-exposure. This information would then be used to describe lead concentrations and correlations in blood and tissues over time. Liver biopsies and diaphragm were also evaluated, and production losses were examined.

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)0.002
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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
GenreOther

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
Published2007
Admission routes2
Has abstractyes

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