Chlorophyll-a concentration climatology, phenology, and trends in the optically complex waters of the St. Lawrence Estuary and Gulf
Bibliographic record
Abstract
The spatiotemporal distribution of phytoplankton biomass drives the marine ecosystem. Chlorophyll-a concentration (Chla) is a biomass index for microalgae in seawater that is commonly used to study phytoplankton by means of satellite remote sensing. The St. Lawrence Estuary and Gulf (SLEG) in Eastern Canada is a highly dynamic subpolar environment characterized by complex marine optical properties that make it difficult to distinguish Chla from the background signal caused by a strong freshwater discharge. In this study, we implement an inverse model based on a set of in situ Chla measurements analyzed by principal component analysis, making it specifically designed for local marine optical conditions. We used this model to convert a multi-mission remote sensing reflectance dataset to daily Chla between 1998 and 2019 at a 4 km spatial resolution. From the resulting Chla time series, we computed the climatology, phenology, and trends over the SLEG. The Chla climatology reveals relatively high Chla in the Gaspé Current, along the Gulf's North Shore, and in areas of strong tidal mixing. Substancial differences in phytoplankton phenology between the various subregions are found, with a prevailing shift towards earlier spring blooms of larger intensities. Finally, we found a positive mean Chla increase of 1.1% y−1 over the SLEG, with strong positive trends in the Magdalen Shallows and west of Anticosti Island. This description of the surface Chla in the SLEG provides important baseline information for the marine ecosystem.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".