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Record W2066690154 · doi:10.4141/a02-084

Copper and health status of cattle grazing high-molybdenum forage from a reclaimed mine tailing site

2003· article· en· W2066690154 on OpenAlexafffundvenue
Wendy C. Gardner, Klaas Broersma, J. D. Popp, Z. Mir, P. S. Mir, Wayne T. Buckley

Bibliographic record

VenueCanadian Journal of Animal Science · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsBrandon UniversityAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsTailingsGrazingForageLand reclamationDry matterBolus (digestion)Animal scienceCopperMolybdenumCopper mineLivestockAgronomyChemistryBiologyEcology

Abstract

fetched live from OpenAlex

High concentrations (21–44 mg kg -1 dry matter) of Mo have been identified in forage in several reclaimed mining areas in British Columbia. Since Mo concentrations greater than 5 mg kg -1 in forage dry matter may result in molybdenosis because of a secondary Cu deficiency in ruminants, a study was undertaken to determine if cattle can safely graze the reclaimed land. For 12-wk grazing periods in 1994, 1995 and 1996, 32 cow/calf pairs grazed high-Mo forage at a reclaimed mine tailings site located at the Highland Valley Copper mine near Logan lake, BC. Half of the animals in the trial received a Cu supplement (All-Trace copper bolus) and the other half served as a control group. There were no significant differences (P < 0.05) in weight gain, liver Mo, serum Cu and Mo, and milk Cu and Mo between the two treatment groups of cows. Liver Cu was higher for the Cu bolus group at certain time periods in 1994 and 1995, indicating that the bolus was effective at supplying Cu. At all times, the liver Cu levels for the animals remained above the recommended critical level of 25 mg kg -1 dry matter. Animals appeared healthy and no signs of Cu deficiency were observed. Key words: Molybdenum, molybdenosis, copper, mine tailings, reclamation, animal health

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.241
Teacher spread0.217 · 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 designBench or experimental
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

Citations20
Published2003
Admission routes3
Has abstractyes

Explore more

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