QUEBEC REGION'S SHORELINE SEGMENTATION IN THE ST. LAWRENCE RIVER: RESPONSE TOOL FOR OIL SPILL
Bibliographic record
Abstract
ABSTRACT The Quebec Region's shoreline description of the St. Lawrence River began in 1985 with the first shoreline interpretation by Environment Canada. This description was available as a paper version and was no longer adequate for oil spill response. An update was required in order to split the shoreline into segments and to digitize the information. A partnership was developed between Environment Canada, Eastern Canada Response Corporation and the Canadian Coast Guard to conduct the aerial survey and to do the segmentation. The cartography of segmentation covers the fluvial part of the St. Lawrence River (Montreal Region) up to the Gulf (including the Lower-North Shore and the St. Lawrence Islands). The database, developed specifically for that project, is oil spill-oriented. It includes geomorphologic information, from the supratidal to the lower intertidal zone, some statistical information and other requirements for the cleanup operation. For this operational database, useful for the response operation, links were developed with other databases and specialized oil spill software. The first system is GENIE Web, which is a Georeference Environmental Network for Information Exchange on the Web. The second system, ShoreAssess©, is a managing tool for SCAT teams in the field. Finally, a partnership with the Geography Department at the Université du Québec in Rimouski (UQAR) will help us to keep the St. Lawrence River coastal evolution up to date.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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".