Biological characterization of the mediolittoral and infralittoral zones of the west coast of Anticosti Island
Bibliographic record
Abstract
This dataset includes measurements of abundance, density, biomass and length of macroalgae from the Algae-WISE project and data on landscape description, macroalgae cover and biomass as well as the abundance and length of benthic macrofauna from the project for the optical and biophysical characterization of the mediolittoral and infralittoral zones of the southwest coast of Anticosti Island. The objective of the Algae-Wise project is to demonstrate the potential of hyperspectral and multispectral imaging for the detection and quantification of three types of marine algal biomass, which are the basis for the productivity and vitality of the ecosystem: phytoplankton, fixed macroalgae and drifting macroalgae. The objective of the optical and biophysical characterization of the mediolittoral and infralittoral areas of the southwest coast of Anticosti Island is to characterize and map shallow coastal ecosystems in order to offer a description of a reference state. In the context of the Algae-WISE project, intensive field campaigns were carried out on the southwest coast of Anticosti Island during the summer period (between June and September) in the summers of 2021, 2022 and 2023. As part of the characterization project, an intensive campaign in July 2024 was carried out on the southwest coast of Anticosti Island. The Algae-Wise project is funded mainly by the Canadian Space Agency (CSA) through the Flights and Fieldwork for the Advancement of Science and Technology (FAST 2019), as well as by Réseau Québec maritime (RQM) for ship-time. The optical and biophysical characterization project of the mediolittoral and infralittoral areas of the southwest coast of Anticosti Island is financed by The Coastal Environmental Baseline Program as part of Fisheries and Oceans Canada's Oceans Protection Plan; The “Satellite-based assessment of coastal ocean productivity” research grants (RGPIN-2019-06070) and “Blue carbon and climate change mitigation through macrophytic coastal ecosystems” (RGPIN-2020-07065, 2020-2026) research grants from the Discovery Grants Program of the Natural Sciences and Engineering Research Council of Canada (NSERC); The research grant “Detection and valorization of stranded and coastal algae: a promising resource for the Quebec blue economy” (WRACK project) from the Maritime Sector Partnership Research Program (2023-2024 competition; # 338154) from the Fonds de Recherche du Québec — Nature and Technologies and the Ministry of Economy, Innovation and Energy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".