Using Fuzzy-Set Classification to Analyse Sea-Level Indicators With Respect to Glacial-Isostatic Adjustment
Bibliographic record
Abstract
The intepretation of sea-level indicators (SLIs) in terms of glacial-isostatic adjustment (GIA) has usually been based on neighbouring SLIs grouped into a single sea-level curve, which is then assumed to represent the Holocene sea-level change in that region. In this method, the nominal height and age of a particular SLI are the only characteristics considered in the inference of the former sea-level height. However, only isolation basins yield a narrow range for sea level, whereas SLIs based on samples, such as flotsam, shells or peat, only allow the determination of an upper or lower bound or a range for it. To use also these types of sample properly, we have developed a classification scheme based on Fuzzy logic. After the defintion of appropriate membership functions, this method leads to a more systematic and realistic interpretation of the large amount of SLIs available. We apply this method to SLIs from several regions in Canada and demonstrate how it modifies the inference of GIA for a particular region and, thus, the determination of mantle viscosity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".