Biogeochemical Data from the Algae-WISE Project (June and July 2022) in the Coastal Waters of Anticosti Island, Gulf of St. Lawrence.
Bibliographic record
Abstract
Biogeochemical data were collected from June 30, 2022 to July 07, 2022 on board the Coriolis II research vessel in the coastal waters of Anticosti Island. This dataset includes biogeochemical parameters analyzed in the laboratory from water samples, including salinity, chlorophyll a, suspended particulate matter, dissolved organic carbon, nutrients, cellular abundance, pigment concentrations, particulate absorption, and absorption of colored dissolved organic matter. The stations are organized into six transects perpendicular to the coast and one transect parallel to the coast. For each station visited, water samples were collected at several depths along a vertical transect, each time including the surface and the corresponding depth of chlorophyll a maximum. It is also possible to consult other data sets related to this project: - Data set of the optical properties of water, measured on board of the Coriolis II ship using a Compact-Optical Profiling System (C-OPS) and a Hyperspectral Surface Acquisition System (HyperSAS) (in preparation) - Optical data measured aboard a watercraft in the waters closer to Anticosti Island are also available: Optical data The Algae-WISE project is funded mainly by the Canadian Space Agency (CSA) through the Flight and Field Investigations in Space Technology and Science program (VITES 2019), as well as by Réseau Québec maritime (RQM) for ship-time.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.019 |
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".