Estimation of phytoplankton loss rate by remote sensing
Bibliographic record
Abstract
A method is presented for estimation of seasonally‐varying, total loss rate of phytoplankton from time series of satellite‐derived phytoplankton biomass data. The loss is calculated as the difference between the (modelled) rate of photosynthesis and the observed, realized rate of change of phytoplankton biomass. A Monte Carlo procedure is used to recover the loss rates. The (biomass‐normalized) total loss rate shows a seasonal cycle with values ranging from 0.5 to 3 mg C (mg Chl)−1 h−1 and shows an abrupt shift during the spring bloom. On the other hand, the absolute loss rate increases during blooms, a consequence of the increase in the biomass. The normalized total loss rate can be further expressed as a time‐varying fraction of the assimilation number. The fraction lies in the range from 0.2 to 0.8. During the increasing (decreasing) phase of phytoplankton blooming, the ratio of growth to total loss increases (decreases), such that this ratio may have value as an ecological indicator for blooms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".