On the way to improving moderate spatial resolution ocean color data nearby highly productive Arctic ice edges
Bibliographic record
Abstract
Phytoplankton plays a crucial role in the world carbon cycle and marine food web. However, impact of global warming on phytoplankton species composition and abundance remains uncertain. This is particularly true in the Arctic Ocean and its marginal seas where global warming tends to be the most pronounced. Ocean color satellite images therefore represent an essential tool for providing a synoptic view of marine environments at spatial and temporal resolutions that traditional sampling methods are unable to acquire. However, over icy waters the quality of satellite images is largely affected by sea ice contamination. Today, the impact of sub-pixel and adjacent sea ice floes on the satellite measured signal are ignored in standard ocean color processing chains resulting in erroneous satellite derived bio-geochemical products. Here we explain how sea-ice affects the quality of satellite ocean color data by comparing in situ water reflectance measurements taken near ice-edges and/or ice-floes with spatial and temporal coincident satellite retrieved water reflectance data. In addition, high and medium spatial resolution satellite data are compared to evaluate the potential to correct ocean color data from sea-ice contamination by taking advantage of the synergy between high and medium spatial resolution images.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".