Distribution of marine organisms in the shallow coastal zone of Quebec surveyed by underwater imagery between 2017 and 2021
Bibliographic record
Abstract
Between 2017 and 2021, eleven underwater imagery sampling campaigns were carried out over an extensive area of the shallow coastal zone of the Estuary and the Gulf of St. Lawrence. These campaigns provided ground-truthing data for the mapping of estuarine and marine macrophytes and were used to document the composition of coastal ecosystems. The imagery acquisition and video analysis methods used are described in this report. In addition, the occurrence data for 150 taxa and categories of marine organisms, as well as the relative dominance of macrophytes, are discussed. The occurrence data and distribution maps highlight general geographic and bathymetric distribution trends for several macroalgae, invertebrate and fish species, including some of commercial interest. Saccharina latissima, frequently observed throughout the study area, regularly dominated the algal beds. In contrast, Chondrus crispus was virtually absent from the St. Lawrence Estuary, but was found to be dominant in the Mingan region and south of Cap Gaspé, indicating a preference for warmer waters. The truncated distribution patterns of certain invertebrates, such as Strongylocentrotus droebachiensis and Homarus americanus, are presumably explained by the presence of limiting physicochemical conditions in certain sectors of the Estuary and the Gulf of St. Lawrence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".