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ASSESSMENT OF WILD TURKEY–HUMAN CONFLICTS THROUGHOUT THE UNITED STATES AND CANADA

2010· article· en· W7084068320 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsnot available
Fundersnot available
KeywordsWildlifeLaw enforcementHuman–wildlife conflictWildlife managementEnforcementFish <Actinopterygii>Agency (philosophy)

Abstract

fetched live from OpenAlex

Abstract: In 2004, the Northeast Association of Fish and Wildlife Administrators charged the Northeast Wild Turkey Technical Committee with investigating wild turkey (Meleagris gallopavo)—human conflicts in urban—suburban communities throughout the wild turkey range. Our objectives were to evaluate the frequency, seasonality, and types of complaints associated with wild turkey—human conflicts in urban—suburban communities; determine the age and sex of wild turkeys generating complaints; and summarize the agencies or organizations responsible for the management techniques used to address these conflicts. We surveyed 49 states and Ontario. Eighty‐two percent of wildlife biologists reported wild turkey—human conflicts in urban—suburban communities. Commonly reported complaints included damage to landscape plantings (23%), wild turkey droppings (15%), aggressive birds making no human contact (12%), aggressive birds making human contact (9%), birds scratching motor vehicles (8%), and birds roosting on rooftops (8%). Wildlife biologists and agency law enforcement personnel primarily were responsible for addressing conflicts. In some jurisdictions, local animal control and municipal law enforcement were involved. The majority of problems were caused by male wild turkeys (60%) and occurred in the winter (47%) and spring (32%). Pen‐raised wild turkeys did not appear to cause many conflicts. Technical advice, physical harassment, and live capture and removal were used to address conflict situations. Agencies used a wide variety of methods to capture and remove wild turkeys, including rocket nets, drop nets, walk‐in traps, long‐handled nets, net guns, firearms, and drugged baits. The increasing incidence of wild turkey—human conflicts requires wildlife managers to continue monitoring conflict situations, evaluating management techniques, and developing proactive approaches, which may include informational brochures and press releases, to mitigate wild turkey—human conflicts.

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)0.002
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 score0.018
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.124
GPT teacher head0.543
Teacher spread0.420 · 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
Published2010
Admission routes1
Has abstractyes

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