Waterfowl conservation planning in the boreal forest: Use of a pre-existing, large-scale, time-series dataset
Bibliographic record
Abstract
Recommended citation: Barker, N. K. S., M. Darveau, and S. G. Cumming. 2010. Waterfowl conservation planning in the boreal forest: Use of a pre-existing, large-scale, time-series dataset. Poster, International Congress for Conservation Biology. Edmonton, AB, Canada. Retreived from figshare: Poster Abstract: Optimal conservation planning should include modelled habitat-species interactions in addition to basic species’ distribution information. The USFWS’ annual Waterfowl Breeding Population and Habitat Survey (WBPHS) provides estimates of waterfowl populations across Canada and the US extending back to the mid-1950s. We assessed this survey’s potential to inform conservation planning in the Boreal by: 1) critically reviewing past research using this dataset; 2) highlighting key questions the survey can address; and 3) identifying potential obstacles and limitations specific to the Boreal. While the survey has historically been used for developing harvest quotas, the focus has shifted to population ecology and species-habitat interactions in recent years, due to advances in spatial analysis and remote sensing. Habitat modelling is a crucial step for identifying priority areas for conservation, and predictive models can assess changes in species populations in response to human activities and climate change. Boreal-specific obstacles include uneven spatial distribution of survey transects, potential gaps in environmental data, and substantially lower population estimates than in the Prairies (which may require Boreal-specific conservation plans). We recommend the collection of ancillary data to improve accuracy of estimates, and we conclude that with detailed and careful analysis, the WBPHS demonstrates great potential for conservation planning in this changing ecosystem.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".