Using Fuzzy Logic for the Inference of the Holocene Land Uplift in the Hudson-Bay Region Based on Sea-Level Indicators
Bibliographic record
Abstract
The Holocene land uplift in regions of Pleistocene glaciation, such as Hudson Bay in Canada, is dominated by glacial-isostatic adjustment with quasi-exponential time dependence. To infer the decay time associated with the uplift, neighbouring sea-level indicators (SLIs) related to the relative sea-level (RSL) height during the Holocene are commonly grouped into a single RSL diagram assumed to be representative of the region considered. Usually, the nominal height and age of a particular SLI are the only characteristics used when determining the Holocene RSL height. However, only SLIs based on isolation basins yield a narrow range for this height, whereas SLIs based on fossil samples, such as shells, peats or drift wood, only allow the determination of an upper bound, a lower bound or a finite interval for it. To use also fossil samples objectively, we develop a classification scheme based on fuzzy logic. After the defintion of appropriate membership functions, this method leads to a more systematic interpretation of the large amount of SLIs available. We apply the scheme to SLIs from the Richmond Gulf region (SE Hudson Bay) near the Pleistocene glaciation center of Canada and derive a decay time of 5.8 ka for the exponential function best fitting the RSL diagram, and thus the Holocene land uplift, for this region.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".