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 disturbance mainly due by logging affects CAWA occurrence and distribution and also the accuracy of the model by ground-truth validation. We used a desktop dataset from different sources, and due to limited number of observations (78 after filtered) we enhanced the dataset with field-collected data gathered in 2021 and 2022. We ran different models also to test the accuracy of the models using only desktop data and a datasete enhances with field-collected data. The SDM’s environmental covariates included a bare soil index (BASI), a normalized water index (NDWI) as an indicator of deciduos vegetation, an enhanced vegetation index (EVI), a digital elevation model (DEM), years since disturbance (DISTURB [usually by logging] 1-20 years since last disturbance happened, 21 value represent undisturbed or no disturbed more than 20 years ago), distance to mature coniferous forest (D_CONIF), tree canopy height (CAN) and distance to water (WATER) as indicator of riparian zones. The models that used field-collected data showed a moderate performance for both training and test data (AUC 0.7) while the model that used only desktop dataset showed a poor performance (AUC 0.6); NDWI, WATER, EVI and D_CONIF were the most influential covariates indicating high association of CAWA to deciduous vegetation, riparian areas, shrub cover and importance of coniferous stands. CAWA occurrence probability was high in undisturbed areas, but also it has a high predicted probability (>0.6) in areas within six years since disturbance; CAWA may take advantage of regenerated forest depending on shrub density and retention of old-growth forest structure ( CAWA had a high prediction of occurrence areas with canopies higher than 10m tall).
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 | 0.001 |
| 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".