Field-validated species distribution model of Canada Warbler (Cardellina canadensis) in Northwestern Ontario
Bibliographic record
Abstract
The Canada Warbler (CAWA) is a species of conservation concern, but its ecological needs and distribution remain poorly understood. Additionally, contradictory findings exist regarding the impact of logging on CAWA abundance and habitat use. Furthermore, its habitat needs may be distorted by limitations in current habitat availability compared to historical conditions. We developed a predictive high-resolution (30 m) field-validated species distribution model (SDM) in Ecoregion 4W of Northwestern Ontario, Canada, where little field-derived information about the species is available. We aimed to assess how time since logging affects CAWA occurrence and distribution. The SDM was built on occurrences from various large datasets (including eBird (2000-2021), Breeding Bird Survey (2000-2019), and Ontario Breeding Bird Atlas (2000-2005) and data from long-term songbird monitoring of Quetico Provincial Park (2014-2019), and we supplemented the dataset with our field collected data from the breeding season of 2021. We filtered the dataset excluding inaccurate coordinates, sites within 250 m, and sites located on the cloud range of Landsat images used. We got a total of 122 observations from 2001 to 2021. The SDM’s environmental covariates included a bare soil index (BASI), a normalized water index (NDWI), an enhanced vegetation index (EVI), a digital elevation model (DEM), years of forest loss (LOSS [usually by logging] ) for forests ˂20 yr old, distance to mature coniferous forest (D_CONIF), and tree canopy height (CAN). We field-validated the model using targeted data collected (30 observations) in the breeding season of 2022.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".