Automated Coastal Ice Mapping with SAR Can Inform Winter Fish Ecology in the Laurentian Great Lakes
Bibliographic record
Abstract
Many freshwater lakes in the temperate zone undergo annual freeze-thaw cycles. Climate change has disrupted these patterns and altered habitat for many species including ecologically, economically, and culturally valuable fish species. To understand the relationship between ice cover and aquatic species, suitable data can be derived from remote sensing. We developed a novel ice classification method with minimal user input using freely available Sentinel-1 data and an adjacent and time-coincident validation dataset. Using image object segmentation and a random forest classifier, ice conditions were classified correctly with >85% overall accuracy. Our ice mapping efforts coincided with a telemetry dataset of tagged Walleye (Sander vitreus) and Northern Pike (Esox lucius) in Hamilton Harbor in western Lake Ontario. Between years with low and high ice covers (2017 and 2019, respectively), we found Walleye appeared to reduce their area of movement when the harbor was covered in ice. Our ice mapping tool can provide a quick and consistent method for agencies to adopt for freshwater resource management as well as provide ice cover information in coastal areas that are important overwintering habitat for many fishes.
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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 | 0.001 | 0.001 |
| 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".