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Record W4308115093 · doi:10.6000/1927-520x.2022.11.04

Description of Four Dual-Purpose River Buffalo (Bubalis bubalis) Production Systems in Tropical Wetlands of Mexico. Part 2: Sanitary Management, Milking, Zootechnical and Economic Indicators

2022· article· en· W4308115093 on OpenAlexvenueno aff
Aldo Bertoni, Adolfo Álvarez-Macías, Diego Armando Morales, J Dávalos, Daniel Mota‐Rojas

Bibliographic record

VenueJournal of Buffalo Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilkworms and Sericulture Research
Canadian institutionsnot available
Fundersnot available
KeywordsMilkingAnimal scienceIce calvingMilk productionToxicologyLivestockAnimal husbandryLactationBiologyAgriculturePregnancyEcology

Abstract

fetched live from OpenAlex

The aim is to elucidate other key aspects of these dual-purpose systems in wetland areas, including labor, markets, the conditions of facilities, machinery and equipment indices, and zootechnical indicators. The health management values determined for production units (PU) PU2, 3, and 4 were similar (50%) but higher at PU1 (75%). Three scheduled milkings once a day (1x), but PU3 performed it twice a day (2x). Most workers are permanent, but PU1 and PU2 hire temporary laborers. The average workday was 7.69 ± 2.84 hours/animal unit at a mean wage of $11.43 ± $1.27. Unit prices per kg of meat from fattening animals and liter of milk were $1.83 ± $0.03 and $0.51 ± $0.08, respectively. Production variables showed an average calving interval of 371.25 ± 7.50 days, a mean parturition index of 89% ± 1%, and mean mortality of 1.8% ± 0.5% and 0.6% ± 0.8% for young and adult animals, respectively. Milk production per lactation was 1240 ± 211.66 liters. The mean daily production for sale was 5.17 ± 0.88 liters. Individual calves consumed 2.13 ± 0.63 liters of milk per day on average. Mean productive life was 17 ± 2.45 years. Average scores on the facilities conditions and machinery and equipment indices were 68% ± 14% and 57% ± 26%, respectively.

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)0.000
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 score0.319
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.026
GPT teacher head0.238
Teacher spread0.212 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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