Coastal cliff erosion vulnerability on the Canadian east coast (Baie des Chaleurs area): a multi-parameter visualization tool
Bibliographic record
Abstract
Abstract In order to assess a cliff&rss vulnerability to erosion, researchers must consider a number of parameters that collectively account for all possible erosional processes. The authors have developed a radial diagram that allows the most active processes of erosion to be visualized (e.g. hydrodynamic, gravity-driven or atmospheric), and when such diagrams are presented on a map, they can be used to rapidly identify the contributing erosional processes at a given location. The diagram, developed for the Baie des Chaleurs region (eastern coast of Canada), displays numerical values that represent the relative importance of various weakening parameters for a set of cliffs. In addition, a colour code represents the dominant lithology, and the diagram diameter is a function of the erosion rate. The data for each diagram are based on field observations, experimental work and results of mineralogical and petrophysical analyses. Ten fundamental parameters were used to assess the structural, petrophysical and environmental processes of erosion: porosity, percentage of matrix or cement, homogeneity of the stratification, presence of schistosity, fracture density, number of fracture sets, presence of faults, dip of the strata, effect of waves, and the presence of groundwater. Coastal managers can use these diagrams in conjunction with natural risk maps to estimate the vulnerability of a cliff and decide whether engineering structures are required for preservation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".