through submarine canyon and fan systems
Bibliographic record
Abstract
Abstract Multibeam sonar data, acoustic sub-bottom pro-files and box cores were used to study the activity of submarine canyons and fans near the city of Les Escou-mins, on the North Shore of the Lower St. Lawrence Estuary (Eastern Canada). The multibeam data were used to generate a high-resolution digital terrain model that reveals the presence of a large number of canyons and fans along the northern slopes of the Laurentian Channel. This paper focuses on two of the larger canyons, and their associated submarine fans. The sub-bottom profiles on the fans reveal high-amplitude reflections at the sediment/water interface and near the seafloor surface, indicating the occurrence of layers of coarse material. A turbidite was observed in a box core sampled in one of the fans, confirming the nature of the coarse layer. Geophysical and sedimentological data indicate that the canyons and fans play an important role in transferring coastal sandy sediments to the deeper marine environments by longshore drift-initiated turbidity flows, and thereby contribute to the negative sediment budget along the coast. The morphology of the canyons indicates that they were produced by a combination of erosive turbidity flows and retrogressive failures. The two box cores sampled on the fans reveal a recent (~last 60 years) quasi-exponential increase in sand content near the surface of the cores, possibly reflecting recent deforestation and/or increased coastal erosion.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".