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Record W2128221582 · doi:10.5539/jas.v1n2p58

Phosphorus Kinetics in Calves Submitted to Single Infection with Cooperia punctata

2009· article· en· W2128221582 on OpenAlexvenueno aff
Hélder Louvandini, Renato Ranzini Rodrigues, Solange María Gennari, Concepta McManus, D. M. S. S. Vitti

Bibliographic record

VenueJournal of Agricultural Science · 2009
Typearticle
Languageen
FieldVeterinary
TopicHelminth infection and control
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsFecesAnimal scienceExcretionBiologyWeight gainPhosphorusBody weightVeterinary medicineEndocrinologyChemistryMedicineEcology

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate phosphorus (P) kinetics in calves submitted to a single acute infection ofCooperia punctata, using isotopic dilution and modelling techniques. Ten Holstein calves were used, with a mean liveweight of 66.05 ± 0.30 kg. Of these, five received a single dose of 45 000 infectant (L3) C. punctata larvae and the otherfive were maintained in a control group without infection. Twenty one days after the infection, all animals received 29.6MBq 32P by intravenous injection to evaluate P kinetics. Weight gain, feed consumption and excretion in the faeces andurine were monitored and blood was collected for seven days. After the collection period, all animals were slaughtered, tissues collected and worms counted. The number of eggs per gram of faeces (EPG) reached 3 342 ± 194 and thenumber of adult worms was 12 992 ± 1 470. Final live weight, mean daily live weight gain, level of P in the plasma andits retention in control calves were higher and P excretion in the faeces less than in the infected calves. There was anegative P balance in both the control and infected calves for soft tissues and bone. A single infection by C. punctatanegatively influenced calf performance and P kinetics, leading to lower retention of the mineral.

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)0.000
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 score0.974
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.281
Teacher spread0.257 · 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

Citations5
Published2009
Admission routes1
Has abstractyes

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