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Record W1581792031

Survey of the status of copper, interacting minerals, and vitamin E levels in the livers of sheep in Ontario.

2003· article· en· W1581792031 on OpenAlexaboutno aff
Paula Menzies, Herman J. Boermans, Brent Hoff, Tiffany Durzi, Lisa Langs

Bibliographic record

VenuePubMed · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsSeleniumZincManganeseAnimal scienceCopperChemistryCadmiumVitaminBiologyBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

Livers from cull ewes and market lambs raised in Ontario were obtained to determine the status of specific minerals and vitamin E. Values for copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn) obtained by atomic absorption and inductively coupled plasma--atomic emission spectroscopy (ICP-AES) were found to be statistically different but sufficiently biologically similar to allow the use of ICP-AES for screening groups of samples for deficient or toxic levels of those minerals. Toxic levels of cadmium were not found. Toxic levels of aluminum were found in 1 cull ewe and 1 market lamb. A significant proportion of both market lamb samples (40.0%) and cull ewe samples (50.0%) had high to toxic levels of Cu. In market lambs, Fe, Mn, molybdenum (Mo), selenium (Se), and Zn were not found to be important determinants of Cu level. In cull ewes, Fe, Mn, and Zn play a moderate role in the variability of liver Cu levels. Selenium was found to be present at marginal levels in 3.3% of cull ewe samples and in 42.6% of market lamb samples. Vitamin E was found to be low to deficient in 10.0% of cull ewe samples and in 90.0% of market lamb samples. In market lambs, only Mo was associated with Se levels, and no minerals were associated with vitamin E levels. In cull ewes, there was a strong association between Se and vitamin E. This survey demonstrates that marked nutritional imbalances of Cu, Se, and vitamin E exist in cull ewes and market lambs in 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 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)0.002
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 score0.131
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.048
GPT teacher head0.230
Teacher spread0.183 · 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

Citations19
Published2003
Admission routes1
Has abstractyes

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