Comment on egusphere-2023-2851
Bibliographic record
Abstract
Abstract. We evaluate the utility of hyperspectral particulate absorption data to characterize phytoplankton community structure in the eastern Subarctic Pacific Ocean. Relative to existing algorithms based solely on Chlorophyll-a concentrations (Chla), improved taxonomic classification (validated with pigment-based data) was obtained by including Principal Components Analysis of hyperspectral absorption data. Multiple linear regression of hyperspectral absorption data yielded better taxonomic classification, particularly for estimates of haptophyte biomass. In addition, size-fractionated hyperspectral measurements were used to determine the dominant phytoplankton size of the phytoplankton community. Using high-frequency ship-board optical data, we examined the spatial patterns in phytoplankton taxonomic abundance in coastal and offshore waters around Vancouver Island, British Columbia. Results from this analysis were consistent with expectations based on previous low-resolution sampling, demonstrating expected seasonal succession of different phytoplankton groups, and significant variability in coastal phytoplankton taxonomy associated with dominant hydrographic features. In contrast, much less spatial and temporal variability was observed in offshore waters. Derived patterns in phytoplankton taxonomy were linked to observed patterns in surface water biogeochemical properties, notably the distribution of dimethyl sulfide (DMS) and dimethylsulfoniopropionate (DMSP) to Chla ratios. Our results highlight the potential for shipboard hyperspectral absorption data to describe phytoplankton community composition and ancillary biogeochemical variables.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.013 | 0.006 |
| Insufficient payload (model declined to judge) | 0.344 | 0.244 |
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".