Chlorophyll Along U.S. West Coast: Natural Hazards
Bibliographic record
Abstract
eoimages.gsfc.nasa.gov/images/imagerecords/14000/14015/California_chlo_SEA_2004265_lrg.jpg Chlorophyll Concentrations (9.41 Mb) The Pacific Coast of North America teams with life because of the rich plant life supported by cold ocean waters. The thriving ecosystem is driven by coastal upwelling where deep ocean water pushes to the surface near the coast. Upwelling is not constant -- it is controlled by the wind. When winds blow from the north, the warm surface water of the ocean is pushed west. Cold ocean water rushes up from the ocean depths to replace the surface water, carrying with it nutrients that had settled on the ocean floor. Called upwelling, the surge of nutrient-rich cold water nourishes tiny ocean plants called phytoplankton. These plants are a source of food for other ocean life. On September 21, 2004, the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) detected high concentrations of chlorophyll along the California, Oregon, and Washington shorelines -- an indication that coastal upwelling was strong. An abundance of phytoplankton have colored the ocean waters dark green in the natural color image shown on the left. On the right, chlorophyll concentrations are shown, with the highest concentrations (dark red) near the shore. From the Strait of Juan de Fuca, which forms the border between Washington and Canada below the bank of clouds in the north, to the southern tip of California on the lower right side of the image, the entire U.S. coastline seems to be affected. NASA images courtesy the seawifs.gsfc.nasa.gov/SEAWIFS.html SeaWiFS Project, Goddard Space Flight Center, and ORBIMAGE
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.019 |
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".