The Hickey Marsh - Characterization of Important Coastal Habitats on the North Shore of the St. Lawrence Maritime Estuary
Bibliographic record
Abstract
A project to characterize important coastal habitats on the north shore of the St. Lawrence maritime Estuary has been funded for a period of 4 years (2018-2022). The aim of this project is to generate ecological reference data to draw a global portrait of the state of seven coastal marshes, namely the [Portneuf-sur-Mer marshes](https://catalogue.ogsl.ca/fr/dataset/5374b582-43aa-4ffa-8361-4a0d9f1f6b8e), the [Mille-Vaches bay](https://catalogue.ogsl.ca/dataset/f10f496a-acf4-4274-9c7b-7a005bcf54ed), the [Pointe des Fortin](https://catalogue.ogsl.ca/dataset/3b8c6d97-6eb8-4e8d-9869-ccb2b9bab5f3), [Bays des Grandes et des Petites Bergeronnes](https://catalogue.ogsl.ca/dataset/ca-cioos_a5125bd3-60e3-4c89-89b6-2d3a8728d10f), [Pointe-aux-Outardes](https://catalogue.ogsl.ca/fr/dataset/46d3ba2e-2fd3-4aad-a51a-f8343fbe6a73) and the Hickey marsh. This dataset covers the Hickey (Colombier) marsh area and associated wetlands. In order to improve knowledge of these ecosystems, a floristic and ichthyological inventory was carried out and the various abiotic factors characterized. Geomorphological data has also been collected, but it is not included in this dataset. However, they remain available by contacting directly the *Comité ZIP de la Rive Nord de l'Estuaire* (RNE) to have access to them. The ZIP RNE Committee holds orthomosaics from the site under study. This project is part of Fisheries and Ocean Canada's Coastal Environmental Baseline Program.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".