Environmental niches and seasonal succession of phytoplankton assemblages in a subarctic coastal bay: Applications to remote sensing estimates
Bibliographic record
Abstract
The seasonal and spatial variability of surface phytoplankton assemblages and associated environmental niches regarding major nutrients, physical (temperature and salinity), and optical characteristics (inherent and apparent optical properties) were investigated in an anthropized subarctic coastal bay, in the Gulf of St. Lawrence: the Bay of Sept-Îles (BSI), Québec, Canada. Seven major phytoplankton assemblages were identified by applying a combined Principal Component Analysis and Hierarchical Cluster Analysis procedures, using pigment concentrations and <20 µm autotrophic cell abundances as inputs. The resulting phytoplankton groups from BSI ( n = 7) were more diverse than at a station monitored in a central portion of the St. Lawrence Estuary ( n = 2). The temporal distribution of the phytoplankton assemblages of BSI reflected the major seasonal (spring to fall) signal of a nearshore subarctic environment. Before the freshet, spring bloom was dominated by large (microphytoplankton) cells (diatoms), and the succession followed a shift towards nanophytoplankton and picophytoplankton cells throughout summer and fall. Most of the phytoplankton assemblages occupied significantly different environmental niches. Taking temperature and the bio‐optical properties (ultimately, the remote sensing reflectance) as inputs, a framework to classify five major groups of phytoplankton in the BSI area is validated. The demonstrated possibility to retrieve major phytoplankton assemblages has implications for applying remote sensing imagery to monitoring programs.
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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 | 0.001 |
| 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) | 0.001 | 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".