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Record W1968839156 · doi:10.3168/jds.2009-2887

Assessing pest control using changes in instantaneous rate of population increase: Treated targets and stable fly populations case study

2010· article· en· W1968839156 on OpenAlexafffundabout
David Beresford, J. F. SUTCLIFFE

Bibliographic record

VenueJournal of Dairy Science · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsTrent University
FundersNatural Sciences and Engineering Research Council of CanadaDairy Farmers of Ontario
KeywordsPEST analysisStable flyPopulationPest controlBiologyToxicologyAgronomyEcologyDemographyBotanyStomoxysMuscidae

Abstract

fetched live from OpenAlex

The instantaneous rate of population increase was used to assess the ability of attractive targets coated with permethrin to control stable fly populations on 3 dairy farms in south central Ontario, Canada. Two attractive targets were deployed over 10 wk in 2001 at each of 6 dairy farms. Three farms were outfitted with 2 untreated targets and 3 were outfitted with 2 targets treated with permethrin [Ectiban-impregnated Coroplast (Ectiban: Schering-Plough Canada Inc., Pointe-Claire, Québec, Canada; Coroplast: Great Pacific Enterprises Inc., Granby, Québec, Canada)]. Population growth rate was measured in terms of degree-days above a 10 degrees C developmental threshold (r(DD10)). The r(DD10) at the 3 treated dairy farms were significantly lower than r(DD10) at the 3 neighboring untreated dairy farms (mean r(DD10): treated=0.0088, untreated=0.013), but not in the previous year when targets were not installed (mean r(DD10): treated=0.012, untreated=0.015). This supports a long-term approach to management that lowers population growth rates in those regions where stable fly numbers increase exponentially from spring until winter, by shortening the period of economic impact of this pest.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.032
GPT teacher head0.291
Teacher spread0.259 · 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
Published2010
Admission routes3
Has abstractyes

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