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Record W2073091382 · doi:10.2981/09-066

A survey of galliform monitoring programs and methods in the United States and Canada

2010· article· en· W2073091382 on OpenAlexaboutno aff
Joseph P. Sands, Michael D. Pope

Bibliographic record

VenueWildlife Biology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsnot available
FundersOregon State University
KeywordsEndangered speciesGeographyPopulationGalliformesEnvironmental resource managementBusinessEnvironmental healthEcologyMedicineEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Abstract We mailed survey questionnaires to 62 upland game bird managers in the United States and Canada in 2004. We received questionnaires from 47 of the 62 (76%) upland game bird managers that were contacted and 43 (91%) respondents provided information on how monitoring data were used. Responses indicated monitoring programs (population trends and/or harvest monitoring) for 23 species of Galliformes, with 145 ± 208 personnel days/year devoted to monitoring. Estimating general population trends (e.g. up or down) was the most frequent objective (N = 41; 95%) of survey data. Other applications of data included assessments of hunting activity, evaluations of regional programs and reviews of conservation status related to Endangered Species Act petitions. The majority of respondents (i.e. 63%) with monitoring programs considered the programs within their states to be effective with respect to their objectives. Many states rely upon hunter surveys, harvest data or road counts to access demographic and population data to address major conservation and management issues. The relevance of these issues is growing and agencies must respond with management recommendations, but often must do so with limited data on the status of their populations. Comprehensive monitoring should be a major component of conservation and management planning for upland gamebird populations particularly as a tool to track and evaluate the effectiveness of management actions and inform management options.

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 score0.036
Threshold uncertainty score0.194

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 communication0.0000.000
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 head0.022
GPT teacher head0.294
Teacher spread0.272 · 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

Citations13
Published2010
Admission routes1
Has abstractyes

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