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Record W2974814219 · doi:10.1371/journal.pone.0222398

Economic and livestock health impacts of birds on dairies: Evidence from a survey of Washington dairy operators

2019· article· en· W2974814219 on OpenAlexaff
Julie L. Elser, Amber Adams‐Progar, Karen M. M. Steensma, Tyler P. Caskin, Susan Kerr, Stephanie A. Shwiff

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsTrinity Western UniversityWestern University
FundersAnimal and Plant Health Inspection ServiceNational Institute of Food and AgricultureWestern SAREU.S. Department of Agriculture
KeywordsLivestockDairy cattleParatuberculosisGeographyHerdVeterinary medicineEnvironmental healthAgricultural scienceBiologyAnimal scienceEcologyMedicine

Abstract

fetched live from OpenAlex

The survey described in this research paper aimed to investigate the economic and health impacts of birds on dairies. Birds are common pests on dairies, consuming and contaminating feed intended for cattle. As a result, dairy operators experience increased feed costs and increased pathogen and disease risk. We surveyed dairy operators attending the 2017 Washington Dairy Conference to examine the impact of birds on dairies in Washington State. Dairy operators reported feed losses valued at $55 per cow resulting in annual losses totaling $5.5 million in the Western region of the state and $9.2 million in the Eastern region of the state. Shooting was the most commonly used bird management method and European starlings (Sternus vulgaris) were the most frequently implicated species statewide. Bird abundance greater than 10,000 birds per day was associated with larger herd size and with self-reported presence of Johne's disease and Salmonella.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.099
GPT teacher head0.273
Teacher spread0.174 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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