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Record W1980124449 · doi:10.5424/sjar/2015131-6613

Cattle mortality due to poisoning in Spain: a cross-sectional epidemiological study

2015· article· en· W1980124449 on OpenAlexaboutno aff
Ricardo García-Arroyo, María-Prado Míguez, M. Hevia, A. Quiles

Bibliographic record

VenueSpanish Journal of Agricultural Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Toxicity and Pharmacological Properties
Canadian institutionsnot available
Fundersnot available
KeywordsDairy cattleEpidemiologyBeef cattleMortality rateLivestockVeterinary medicineQuarter (Canadian coin)Cattle DiseasesAnimal scienceBiologyGeographyMedicineEcologySurgery

Abstract

fetched live from OpenAlex

The lack of nationwide public databases on poisoning in cattle makes it difficult to investigate this issue. Hence, we conducted an epidemiological study using the data on cattle poisoning provided by an insurance company (2000-2005), to determine the mortality rate due to poisoning in cattle in Spain and to assess the influence of the following variables: type of farming, age, sex, time of year, year and region. We observed a mortality rate of 23.25 per 100,000 animals in Spain with a higher rate in beef than dairy cattle (32.14 vs. 4.51 per 100,000 animals). There were also differences in the mortality rate between breeding cattle and future breeders, affecting dairy and beef cattle in a different way. In dairy cattle, we found differences between the years analysed. In beef cattle, the time of year with highest risk of poisoning was the last quarter (19.45 per 100,000 animals), while the lowest mortality rate was observed in the first quarter (1.33 per 100,000). There were pronounced differences between regions in beef cattle, differences being non-significant in dairy cattle. Lastly, in beef cattle, no differences were found between sexes. In summary, the mortality rate due to poisoning in cattle in Spain is low, and the risk of poisoning is determined by the farming system, animals’ stage of development, time of year and region.

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)0.006
metaresearch head score (Gemma)0.003
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 score0.242
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
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 integrity0.0000.001
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 head0.273
GPT teacher head0.454
Teacher spread0.181 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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