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Record W2460219751

Acute lead poisoning in western Canadian cattle - A 16-year retrospective study of diagnostic case records.

2016· article· en· W2460219751 on OpenAlexaffabout
Vanessa Cowan, Barry Blakley

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsLead poisoningMedicineAnimal scienceVeterinary medicineInternal medicineBiology
DOInot available

Abstract

fetched live from OpenAlex

This study describes the epidemiology of acute lead poisoning in western Canadian cattle over the 16-year period of 1998 to 2013 and reports background bovine tissue lead concentrations. Case records from Prairie Diagnostic Services, Western College of Veterinary Medicine, identified 525 cases of acute lead toxicity over the investigational period. Poisonings were influenced by year (P < 0.0001) and month (P < 0.0001). Submissions were highest in 2009 (15.6%), 2001 (11.2%), and 2006 (9.9%). Most cases were observed during May, June, and July (62.3%). Cattle 6 months of age and younger were frequently poisoned (53.5%; P < 0.0001). Beef breeds were predominantly poisoned. Mean toxic lead concentrations (mg/kg wet weight) in the blood, liver, and kidney were 1.30 ± 1.70 (n = 301), 33.5 ± 80.5 (n = 172), and 56.3 ± 39.7 (n = 61). Mean normal lead concentrations in the blood, liver, and kidney were 0.036 ± 0.003 mg/kg (n= 1081), 0.16 ± 0.63 mg/kg (n = 382), and 0.41 ± 0.62 mg/kg (n = 64).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.223
Teacher spread0.207 · 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 teacher head, not a consensus.

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

Citations25
Published2016
Admission routes2
Has abstractyes

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