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Record W2549046264 · doi:10.5539/jas.v8n12p127

Chemical Composition of Drilled Wells Water for Ruminants

2016· article· en· W2549046264 on OpenAlexvenueno aff
Daniel Bomfim Manera, Tadeu Vinhas Voltolini, Daniel Ribeiro Menezes, Gherman Garcia Leal de Araújo

Bibliographic record

VenueJournal of Agricultural Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsnot available
Fundersnot available
KeywordsChemical compositionEnvironmental chemistryComposition (language)Trace MineralsMineralMineralogyChemistryAridEnvironmental scienceRuminantBiologyEcologyFood science

Abstract

fetched live from OpenAlex

This study aimed to evaluate the chemical composition of water wells and to discuss the results in relation to nutritional requirements and tolerance limits of domestic ruminants. Ten samples of water wells (three replicates) from Brazilian semi-arid were collected and analyzed for their macro and trace minerals levels. A variation was found in the mineral composition of the waters and the macro minerals presenting highest levels were Cl, Mg, Ca and Na, while the predominant trace minerals were Fe and Mn. The concentration of the examined minerals can provide a small contribution to the animal as in the case of P or supply a considerable amount as Cl. The levels of total dissolved solids found in the majority of the samples can be tolerable for ruminants. In some of the samples the presence of Pb, Cd and Cr was found in concentrations higher than the upper recommended limit for ruminants.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.211
Teacher spread0.203 · 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 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

Citations4
Published2016
Admission routes1
Has abstractyes

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