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Record W2958237555

Non-Lethal Harassment to Disperse Canada Geese in Winter Wheat Fields

2003· article· en· W2958237555 on OpenAlexaboutno aff
Amy Villano, David Drake

Bibliographic record

VenueUtah State Research and Scholarship (Utah State University) · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWinter wheatAgronomyHarassmentBiologyPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

Canada goose (Branta canadensis) populations have increased dramatically in the Atlantic Flyway during the last 50 years, primarily due to large increases in nonmigratory, resident Canada goose populations. Among the associated problems with the increase in goose numbers is grazing and trampling damage to forage crops like winter wheat. Reducing or eliminating goose presence on winter wheat fields is valuable to farmers because goose damage can result in a loss of yield and increased soil erosion. The objective of our study was to evaluate the efficacy of 3, non-lethal harassment techniques (flagging, propane cannon, and combination of flagging and propane cannon) to reduce or eliminate goose presence on New Jersey winter wheat fields. The study was conducted between December 2000 and January 2001. We measured the efficacy of each treatment based on the height of winter wheat within randomly placed 1-m 2 quadrants, as compared to a control field. We also evaluated the cost of each management option. All non-lethal treatments were statistically different (P = 0.05) relative to the control field in terms of increased wheat height. The cost for purchasing and installing the treatments ranged from $6/ac for the flags to $111/ac for the propane cannon. We recommend the use of a propane cannon, where practical, as a first resort to reduce goose grazing of winter wheat. Flagging, however, can be a cost-effective non-lethal management option to reduce goose damage to winter wheat.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.288
Teacher spread0.253 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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