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Record W7057280953

Investigation of environmental factors and their effects on turkey health

2000· dissertation· en· W7057280953 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2000
Typedissertation
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsBarnRelative humidityLitterLivestockStatistical analysisHumiditySignificant difference
DOInot available

Abstract

fetched live from OpenAlex

During the first stage of this study, a complete statistical analysis was performed on condemnation records from 1991-1997 at the four main processing plants in Ontario. Results of this part of the investigation indicated that airsacculitis was responsible for 14-36.5 percent of all condemnations, followed by cellulitis, which accounted for 14-21 percent of all condemnations. Toms showed a significantly higher condemnation rate (4.3 percent) than hens and broilers with 1.4 and 1.2%, respectively. Regarding airsacculitis, there was a significant difference of seasonal effect, being highest in winter and lowest in fall. In the second stage of this study, trends of important environmental factors (ammonia levels in air, litter moisture, relative humidity and temperature and total bacteria and 'E. coli' counts in the litter) at different ages and their effects on the condemnation rates due to airsacculitis and cellulitis were assessed. High concentrations of ammonia in the grower barn clearly increased the condemnation rates of airsacculitis in turkey flocks. Total 'E. coli' counts in the litter of the grower barn appeared to be an important factor in increasing the prevalence of cellulitis. It was also shown that lower levels of litter moisture and lower relative humidity had positive effects on the condemnation rates due to cellulitis.

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)0.001
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 score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.007
GPT teacher head0.179
Teacher spread0.171 · 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

Citations0
Published2000
Admission routes1
Has abstractyes

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