A revised coastal sensitivity index for Canada’s marine coasts calculated using nonparametric statistics
Bibliographic record
Abstract
A coastal sensitivity index (CSI) is a measure of the sensitivity of a coastline to its physical environment, which provides information useful for coastal management. Traditionally, a CSI is calculated as the mathematical aggregation of coastal sensitivity indicators, which may include factors such as coastal material, relief, and wave energy. The indicators differ depending on study area, but generally are assigned a score ranging from one to five in order of increasing sensitivity. These scores are then aggregated using either the square root of the product mean (the “classic” method) or the geometric mean. Both of these methods are limited by mathematical assumptions, lack of comparability, and the need for empirical validation. In this study, we applied an alternative nonparametric method of calculation, known as μ-statistics, to Canada's marine coasts to provide an improved measure of coastal sensitivity. μ-statistics, which offer a mathematically sound method of aggregating ordinal indicators, have a number of theoretical advantages over the classic and geometric mean methods. In practice, when applied to Canada’s marine coasts, we find that the μ-statistics method (1) compresses the mid-range variability in the resulting sensitivity index, (2) accentuates positive and negative distribution tails, and (3) minimizes propagated errors by 190% and 50%, respectively, compared with the classic and geometric mean methods. Additionally, the μ-statistics method has a theoretical foundation that relieves the necessity to empirically validate the aggregating assumptions and relies only on the assumptions inherent in the scoring method. μ-statistics thus provide a new, rigourous method for the calculation of coastal sensitivity indices when the underlying variables have ordinal scores.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".