Tectono-Geomorphic Analysis in Low Relief, Low Tectonic Activity Areas: Case Study of the Temiskaming Region in the Western Quebec Seismic Zone (WQSZ), Eastern Canada
Bibliographic record
Abstract
We designed a workflow to investigate areas of potential neotectonic deformation, making use of well-developed techniques, but applied to a site characterized by low relief and low or moderate tectonic activity. In this pilot study, we targeted the Temiskaming Graben, in Eastern Canada, where recent and ongoing geophysical and sedimentological investigations have revealed recent activity along this ancient structure. The dataset compiled for this experimental study covers an area of nearly 147 square km across the provinces of Ontario and Quebec. For efficiency in terms of computational resources, we first performed cluster analysis on knickpoint location, identifying seven areas with a high density of disruptions along river profiles. We then performed more detailed morphometric analysis at 30 m resolution, identifying knickpoints along river profiles, calculating the hypsometric integral across the landscape with a moving window, and mapping and comparing lineaments with known structural features. The results of our workflow showed that these three techniques can be efficiently combined for neotectonic analysis, and the synergistic approach strengthens the reliability and accuracy of our results. Our research extends the application of morphometric analysis, commonly used for exploring areas with intense tectonism and high topography, to areas that are characterized by low relief and low or moderate tectonic activity. The new areas identified with the workflow proposed in this research require ground-truthing through mapping and shallow geophysical investigations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".