Analyzing spatial patterns of thermal alteration in the Stikine and Wrangell terranes of the Canadian Cordillera using the conodont color alteration index (CAI) to identify hot spots and cold spots
Bibliographic record
Abstract
Spatial analysis has been conducted on CAI data from archival conodont collections across the Canadian Cordillera of British Columbia and Yukon, creating thermal alteration maps with Kriging interpolation surfaces and Getis-Ord Gi* statistical hot spots. Major hot spots include the southeast corner of British Columbia, the central coast of British Columbia, southern Vancouver Island, and most of the British Columbia - Yukon border. Major cold spots include the northeast quadrant of British Columbia, the northern tip of Vancouver Island, and central Haida Gwaii. Hot spots on Vancouver Island generally correlate with the prevalence of intrusive units nearby; however, the largest hot spot coincides with a southern region unique on the island for having significant outcroppings of Permian limestone, which is more heavily altered than the Triassic limestones commonly sampled for conodonts further north on the island. Comparison of locations where paleoenvironmental studies have utilized delta-13C, both near the British Columbia - Yukon border, as well as at the far north of Vancouver Island, demonstrates that stratigraphic sections which preserves a primary delta-13C signal tends to be situated closer to the center of a thermal alteration cold spot than sections that do not. Beyond the select examples discussed in this study, the broader analysis has potential applications in a wide variety of research, from Cordilleran tectonics to preliminary hydrocarbon exploration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".