Particulate light absorption and the prediction of phytoplankton biomass and planktonic metabolism in northeastern Spanish aquatic ecosystems
Bibliographic record
Abstract
Examination of particulate light absorption and microplankton metabolism in 36 northeastern Spanish aquatic ecosystems, ranging from alpine rivers to inland saline lakes and the open Mediterranean Sea, revealed the existence of general relationships between particulate light absorption and the biomass of phytoplankton and microplankton metabolism. The particulate absorption spectra reflected a dominance of nonphotosynthetic, likely detrital, particles in rivers and a dominance of phytoplankton in coastal lagoons. There was a strong relationship between the light absorbed by phytoplankton and the chlorophyll a (Chl a) concentration of the systems, which indicated an average (±SE) Chl a specific absorption coefficient of 0.0233 ± 0.0020 m2·mg Chl a-1 for these widely diverse systems. Chl a concentration was a weaker predictor of the total particulate light absorption coefficient, pointing to an important role of nonphytoplanktonic particles in light absorption. Gross production was very closely related to the light absorption coefficient of phytoplankton, whereas community respiration was strongly correlated with the total particulate light absorption coefficient, indicating the optical signatures of sestonic particles to be reliable predictors of planktonic biomass and metabolism in aquatic ecosystems.
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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 | 0.000 |
| 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".