Nearshore sediment transport as influenced by changing sea ice, north shore of Prince Edward Island, Canada
Bibliographic record
Abstract
This study considers the influence of changing sea ice on nearshore sediment transport in the central north shore of Prince Edward Island, Southern Gulf of St. Lawrence. It is widely accepted that changing climate is causing sea ice to decrease in the region. Sea ice attenuates wave energy thereby reducing sediment transport. The Delft3D hydrodynamic model is used to simulate waves, currents, and sediment transport in seven sea ice concentration scenarios that can be differentiated into four classes: open water (<10%), low ice (10% to 35%), moderate ice (36% to 60%), and high ice (>60%). If ice concentration decreases from high to moderate, sediment transport is expected to increase 23%. Similarly, if ice concentration decreases from moderate to low, sediment transport is expected to increase a further 24%. If ice concentration decreases from low to open water conditions, sediment transport is expected to abruptly increase a further 85%. The increase in sediment transport as sea ice decreases from high concentration to open water conditions is 180%. Linear and power-law fits of sediment transport and sea ice concentration intersect at an ice concentration of 30%, indicating this value may be a useful threshold in planning for increased coastal erosion and developing appropriate adaptation strategies, in particular, adapting to increased sediment transport near tidal inlets and navigation channels.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".