A survey of galliform monitoring programs and methods in the United States and Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".