Absorption properties of phytoplankton in the Lower Estuary and Gulf of St. Lawrence (Canada)
Bibliographic record
Abstract
Models of ocean colour rely on information about phytoplankton absorption, which varies according to community composition and photoacclimation. Here we show that pigment packaging, which is strongly determined by the size structure of local algal populations, represents a dominant factor in the Estuary and Gulf of St. Lawrence, accounting for ~50%–80% of the reduction in phytoplankton absorption at 440 nm during the spring bloom periods and for 24%–48% before and after the blooms. This is consistent with the importance of diatoms in this environment. Comparison between three methods of estimating packaging gave average values within less than 20% of each other during the blooms. Changes in pigment composition, which also affect phytoplankton absorption, were more important outside bloom periods (particularly in the Gulf), although this influence was relatively modest (11%–13%). This was accompanied by an increase in photoprotective pigments and an absorption peak in the ultraviolet range (~330 nm). Regional variations in phytoplankton absorption reflected bloom conditions, whereas detrital particulate material was highest in the upstream Saguenay region (often more than 60% of the absorption of total particulate material at 440 nm (ap(440))) and was at least 20%–30% elsewhere. This information is a first step towards the development of regional models of ocean colour.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".