Uncertainty assessment: Application to the shoreline
Bibliographic record
Abstract
It is impossible to know beforehand the planforms of a stretch of beach without being first aware of the maritime climate affecting it. This article describes a procedure for objectively calculating the uncertainty associated with the prediction of the evolution of a stretch of beach in terms of probability. On the basis of oceanographic data records as well as empirical orthogonal functions (EOF), we propose a procedure for the simulation of possible sequences of storm events. Such sequences were then entered as input for a morphodynamic model with a view to the subsequent generation of possible planforms. EOF methodology was then used to estimate the probability of each of the planforms thus generated. The case study presented here is that of the evolution of an initially straight sand beach where a rectangular tapered fill had been constructed. The beach is located upshore of a groin perpendicular to the coastline, and had blocked all longshore sediment transport. For this analysis we used a one-line model with time-dependent boundary conditions and a non-homogeneous diffusion coefficient.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".