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Record W3133294584 · doi:10.3390/insects12020159

Operational Parameters for the Aerial Release of Sterile Codling Moths Using an Uncrewed Aircraft System

2021· article· en· W3133294584 on OpenAlexaffabout
Evan D. Esch, Rachael Horner, Dustin Krompetz, Nathan Moses-Gonzales, Melissa R. Tesche, D. M. Suckling

Bibliographic record

VenueInsects · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersInternational Atomic Energy Agency
KeywordsCodling mothBiologySterile insect techniquePEST analysisIntegrated pest managementAerial applicationMating disruptionPest controlBiological pest controlHorticultureLepidoptera genitaliaToxicologyAgronomyPesticideEcology

Abstract

fetched live from OpenAlex

The codling moth is a serious pest of apples in most regions of the world where this fruit is produced. The sterile insect technique is one strategy used to control this pest and is employed as part of an area-wide integrated pest management program for the codling moth in British Columbia, Canada. Modified fixed wing aircraft are the most common method for the release of sterile insects in large area-wide pest management programs. However, aerial release with a full-size aircraft can be prohibitively expensive. We evaluated the use of small, uncrewed aircraft systems (UASs) for the release of sterile codling moths. Sterile codling moths released from greater altitudes were more broadly distributed and drifted more in strong winds, compared to those released from lower altitudes. Most of the released insects were recaptured in a 50 m wide swath under the release route. Recapture rates for aerially released insects were 40-70% higher compared to those released from the ground. UASs provide a promising alternative to ground release and conventional aircraft for the release of sterile codling moths.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.039
GPT teacher head0.244
Teacher spread0.205 · 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 designBench or experimental
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
Published2021
Admission routes2
Has abstractyes

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