Shore Platform Processes in Eastern Canada
Bibliographic record
Abstract
This research is conducted on a mesotidal, argillite shore platform at Mont Louis in Gaspé, Québec, and on macrotidal platforms in the basalts of Scots Bay and the sandstones of Burncoat Head in Nova Scotia. Rock samples have been subjected to wetting and drying and to salt weathering cycles. The platforms were surveyed; rock hardness was determined with a Rock Test Hammer; waves were recorded in the field; and downwearing rates were measured at 56 micro-erosion meter stations over 1 to 3 years. Weathering is the dominant process at Mont Louis, although the horizontal platform may have been cut by waves at the high tidal level. Wave backwearing was much more important than downwearing by weathering during the Holocene at Scots Bay. Wave quarrying only occurs on a few scarps today, however, and without much abrasive material, slow downwearing now dominates over most of the platform surface. Waves probably help to remove loosened sand grains at Burncoat Head, thereby contributing to platform downwearing. Abrasion is also important in places, but the data suggest that backwearing by wave quarrying and probably frost has been a little more important than downwearing by abrasion and weathering during the Holocene.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".