Use of Synthetic Aperture Radar (SAR) to Identify and Characterize Overwintering Areas of Fish in Ice‐Covered Arctic Rivers: A Demonstration with Broad Whitefish and Their Habitats in the Sagavanirktok River, Alaska
Bibliographic record
Abstract
Abstract In northern climates, locating overwintering fish can be very challenging due to thick ice cover. Areas near the coast of the Beaufort Sea provide valuable overwintering habitat for both resident and anadromous fish species; identifying them and understanding their use of overwintering areas is of special interest. Synthetic aperture radar (SAR) imagery from two spaceborne satellites was examined as an alternative to radiotelemetry for identifying anadromous fish overwintering. The presence of water and ice were sampled at 162 sites, and fish were sampled at 16 of these sites. From SAR imagery alone, we successfully identified large pools inhabited by overwintering fish in the ice‐covered Sagavanirktok River, Alaska. In addition, the imagery was able to identify all of the larger pools (mean minimum length = 138 m, SD = 131, range = 15–470 m) of water located by field sampling. The effectiveness of SAR in identifying these pools varied from 31% to 100%, depending on imagery polarization, the incidence angle range, and the orbit. Horizontal transmit—vertical receive (HV) polarization appeared to be best. The accuracy of SAR was also assessed at a finer pixel‐by‐pixel scale (30 × 30 m). The best correspondence at this finer scale was obtained with an image having HV polarization. The levels of agreement ranged from 54% to 69%. The presence of broad whitefish Coregonus nasus (the only anadromous species present) was associated with salinity and pool size (estimated with SAR imagery); fish were more likely to be found in larger pools with low salinity. This research illustrates that SAR imaging has great potential for identifying under‐ice overwintering areas of riverine fish. These techniques should allow managers to identify critical overwintering areas more easily and at lower cost than traditional techniques permit.
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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 | 0.000 |
| 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) | 0.000 | 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".