Modelling Waterfowl Abundance Within The NASA Above Domain
Bibliographic record
Abstract
The Arctic-Boreal zone (ABZ) is a vast landscape, supporting many waterfowl species. However, because of spatial extent and data scarcity, modelling waterfowl populations across the ABZ is challenging. Species abundance models (SAMs) for waterfowl typically use time-static habitat covariates to make predictions, such as wetland type maps. Here, for the first time, we used remote sensing methods to create time-varying ABZ wetland inundation covariates, which helped improve our waterfowl SAMs. SAMs were tested over the Peace Athabasca Delta (PAD), within the NASA Arctic Boreal Vulnerability Experiment (ABoVE) domain. Generalized Additive Mixed Models (GAMM) were used to demonstrate the efficacy and additive value of this novel inundation data, which was derived using Google Earth Engine (GEE) and Sentinel-1 satellite imagery. Timely inundation data like these provide an opportunity to better understand the spatio-temporal variation in ABZ waterfowl under changing climatic conditions.
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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 | 0.001 |
| 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) | 0.001 | 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".