Highly Resolved Surface Phytoplankton Community Composition Along the British Columbia Coast, Derived From In Situ Hyperspectral Radiometry
Bibliographic record
Abstract
Abstract Quantitative measurements of phytoplankton community composition (PCC) are essential for understanding fisheries production, ocean nutrient cycling, and the export of particulate carbon to the ocean interior. However, these measurements are constrained in dynamic coastal waters due to the spatial‐temporal constraints of in situ sampling, difficulty quantifying communities, and the challenges of deriving community compositions via satellites. Here, we work to address these issues by using highly resolved in situ hyperspectral radiometry, along a ship of opportunity track through Case‐2 waters of the Strait of Georgia (SoG) British Columbia, to derive phytoplankton community composition. First, an empirical orthogonal function (EOF)‐based algorithm was developed using HPLC CHEMTAX‐derived phytoplankton group‐level chlorophyll‐a (Chla) and Total Chla (TChla) concentrations and corresponding principal components derived from hyperspectral remote sensing reflectance. Second, the outputs were evaluated using cross‐validation, showing good retrievals for TChla and the regionally dominant phytoplankton groups: diatoms, cryptophytes, green algae, and raphidophytes, which followed expected spatial‐temporal trends with diatom‐dominated spring blooms and succession to high diversity flagellate‐dominated summer conditions. Furthermore, the outputs captured fine spatial scale trends including strong harmful raphidophyte blooms over the narrow transition to low salinity Fraser River plume influenced waters. These findings highlight the potential of using highly resolved hyperspectral radiometry to derive fine‐scale trends in phytoplankton group level community composition in optically dynamic coastal waters. Coupled with additional measures, this method could provide valuable information on phytoplankton dynamics in the SoG, which is a critical habitat for a high diversity of pelagic fish species, including Pacific salmon.
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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 | 0.001 | 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".