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Comparison of Sample Units for Estimating Population Abundance and Rates of Change of Adult Horn Fly (Diptera: Muscidae)

2000· article· en· W2176095347 on OpenAlexaff
Tim Lysyk

Bibliographic record

VenueJournal of Medical Entomology · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsMuscidaeHaematobia irritansBiologySample (material)StatisticsAbundance (ecology)PopulationSampling (signal processing)Sample size determinationAnimal scienceEcologyMathematicsDemography

Abstract

fetched live from OpenAlex

This study compared the reliability of population estimates of adult horn fly, Haematobia irritans (L.), obtained using different sample units. Mean-variance relationships were similar for abundance estimates obtained by counting flies on the sunny sides of cattle, on the upper body, and on the whole animal. Precision varied among the sample units, and was lowest for estimates obtained using the sunny side. Abundance estimates obtained using the sunny side and upper body sample units were related to estimates obtained using the whole body sample unit. However, the proportion of flies in the upper body and sunny side sample units declined with increasing fly density. Seasonal movement toward the belly accounted for this decline. This movement resulted in bias in estimating rates of change based on counting flies on the sunny side and upper body sample units. Rates of change based on sampling the sunny side were more biased than estimates based on the upper body sampling unit. Bias in estimating rates of change was examined using an analytical model compared with field data, and resulted from changes in the proportion of flies occupying the sample unit. Bias also increased with increasing actual rates of change. The implication of these findings for studying horn fly populations are discussed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.348
Teacher spread0.266 · 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

Citations11
Published2000
Admission routes1
Has abstractyes

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