The Pointe-aux-Outardes Marsh - Characterization of Important Coastal Habitats on the North Shore of the St. Lawrence Estuary
Bibliographic record
Abstract
A project to characterize important coastal habitats on the north shore of the St. Lawrence Estuary was funded for a period of 4 years (2018-2022). The purpose of this project is to generate reference ecological data to draw a global portrait of the state of coastal marshes in the upper north shore sector of Quebec. This dataset covers the Pointe-aux-Outardes marsh area and related wetlands. In order to improve knowledge of these ecosystems, flora and fauna (ichthyological and benthic) inventories have been carried out and the various abiotic factors characterized. Geomorphological data was also collected, but is not included in this dataset. However, they remain available, contact the ZIP Committee of the North Shore of the Estuary (RNE) directly to access them. The RNE ZIP Committee also holds orthomosaics of the downstream section of the Pointe-aux-Outardes marsh and the littoral spit, transects perpendicular to the coast covering the entire littoral marsh (dGNSS Reach), two ecogeomorphological limits (lower limit of the vegetation of the marsh and the coastline) as well as oblique surveys of the western sector of the Manicouagan Peninsula (sector from Pointe du Bout to the old dock of the municipality of Pointe-aux-Outardes) (MavicMini DJI). This project is part of the Coastal Environmental Baseline Program Initiative under the Oceans Protection Plan of Fisheries and Oceans Canada.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".