Mapping the estuarine and marine macrophytes of Quebec
Bibliographic record
Abstract
Eelgrass beds, salt marshes and macroalgae beds in the shallow coastal zone (depths of <10 m) of the Estuary and Gulf of St. Lawrence in Quebec were mapped to support preparedness and response in the event of oil spills. The data were acquired in partnership with Université du Québec à Rimouski over more than 4,200 km of coast. The mapping method developed used very high-resolution (30 cm) multispectral aerial photography (red, green, blue, infrared) of coastal ecosystems, with the resulting images automatically segmented and then photointerpreted. Oblique photographs and field data were then used to validate or adjust the values obtained from photointerpretation. The mapping and underwater imagery analysis methods developed allowed recent data (2015–2021) to be acquired on vegetated environments and substrates on Quebec’s maritime coast. The diversity of environments mapped and the level of detail obtained provide an overview of the spatial distribution of vegetated environments vulnerable to environmental incidents. The mapping products generated as part of this project are openly available on the Open Government Portal and SIGEC Web.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.005 | 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".