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Record W4396571920 · doi:10.1139/dsa-2023-0104

Precision pest control using purpose-built uncrewed aerial system (UAS) technology and a novel bait pod system

2024· article· en· W4396571920 on OpenAlex
Craig Morley, Philip Solaris, Greg Owen Quinn, Kathryn Ross, Bruce J. Peterson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDrone Systems and Applications · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
FundersAgricultural and Marketing Research and Development Trust
KeywordsPoint of deliveryPEST analysisPest controlRemote sensingComputer scienceGeographyBiologyAgronomyHorticulture

Abstract

fetched live from OpenAlex

Controlling invasive species is imperative due to their significant roles in spreading diseases, preying on threatened species, and diminishing biodiversity. Crewed aircraft are proficient at dispersing toxic bait across vast expanses to combat small pest mammals such as possums and rats. However, their utility diminishes significantly in small, remote areas typified by rugged terrain due to impracticality and prohibitive costs. Similarly, while ground control operations are effective in compact, easily reachable locations, they encounter formidable obstacles like costly labour expenses, safety hazards, and the peril of worker injuries while navigating treacherous landscapes. An innovative approach to address these limitations is to use uncrewed aerial systems that are unhampered by the terrain to deploy bait at precise locations. Our team engineered a purpose-built system designed specifically for deploying bait using innovative bait pods. Two field trials were conducted in New Zealand to validate our systems’ efficacy, assessing deployment precision and accuracy against predefined ground targets. While the initial trial yielded mixed results, significant improvements were observed in the subsequent trial, featuring enhancements to the bait pod design. The median deployment accuracy achieved was 1.91 m from the target ( n = 63), with no statistically significant difference in deployment accuracy between open and forested areas ( p = 0.76). This advanced system permits the precise placement of bait pods to any location, facilitating effective pest control within complex landscapes, challenging terrain, and dense vegetation. With its smart functionality and adaptability, this system can be utilised across various aircraft and autopilot systems to ensure maximum accuracy and efficiency in delivering bait pods for pest control operations. Therefore, this innovative tool possesses tremendous potential for managing small mammalian invasive species, particularly in specialised environments such as reserves, gullies, and islands, complementing existing pest control strategies to expedite the restoration of ecosystems and safeguard biodiversity.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score0.608

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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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