Wetland birds in the northern prairie pothole region may show sensitivity to agriculture
Bibliographic record
Abstract
Abstract Wetland losses in the Northern Prairie Pothole Region (NPPR) are largely attributed to agriculture. Since land-use is known to influence bird habitat selection, bird community composition is likely sensitive to the extent of neighboring agricultural activity. We determined which local and landscape habitat variables are most predictive of wetland bird assemblage occurrence in southern Alberta. We:1) identified distinct bird assemblages with a cluster analysis, 2) identified which species were indicative of these assemblages using an indicator species analysis and 3) predicted which bird assemblage would occur in a wetland with a classification and regression tree. Avian assemblages were more loosely defined and had few indicator species. Importantly, assemblages were specific to the natural region in which the wetland occurred. Also, landscapes with higher agricultural activity generally supported waterfowl and shorebirds, likely because agricultural activities excluded wetland-dependent birds that nest in upland habitat. Though waterfowl and shorebirds show poor sensitivity to surrounding landscape composition, edge-nesting wetland avifauna may make good indicators of ecological integrity.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".