Using Fuzzy Logic for the Analysis of Sea-level Indicators with Respect to Glacial-isostatic Adjustment: An Application to the Richmond-Gulf Region, Hudson Bay
Bibliographic record
Abstract
An important constraint for the inference of mantle viscosity is the variation of the Holocene relative sea-level (RSL) height (with respect to today) following the last deglaciation. As a measure of this variation, sea-level indicators (SLIs) related to the RSL heights at specific past time epochs are used. For the inversion of the RSL-height change in terms of mantle viscosity, neighbouring SLIs may be grouped into an RSL diagram taken as representative for the region considered. Usually, the nominal height and age of a particular SLI are the only characteristics considered when determining the former RSL height. However, only SLIs based on isolation basins yield a narrow range for this height, whereas SLIs based on fossil samples provide a lower bound (shells), an upper bound (driftwood) or a finite interval (basal peat) for it. To also use fossil samples objectively, we develop a classification scheme of the depositional conditions based on fuzzy logic. After the definition of appropriate membership functions, this method leads to a systematic interpretation of the large number of SLIs available. We apply this method to SLIs from the Richmond-Gulf region, southeastern Hudson Bay, near the former glaciation center of Canada and derive a decay time of 5 ka for the exponential function best fitting the RSL diagram for this region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".