Recent warming in northwestern Ontario inferred from borehole temperature profiles
Bibliographic record
Abstract
We have used the temperature depth profiles available in the region north and northwest of Lake Superior, in Ontario, Canada, to reconstruct the changes in ground surface temperature over the past 500 years. The 49 temperature depth profiles used were obtained for heat flow measurements and are part of two different data sets, one collected around 1980, the other one after 2000. We have discarded 16 of these profiles because of known nonclimatic perturbations (lakes, topography, clear signs of groundwater circulation). We have inverted the remaining 33 profiles to infer the variations in ground surface temperature. Individual and joint inversions consistently show a recent (150–200 years) warming (1–2 K) of the ground surface. This warming trend is similar to that inferred for northern Manitoba and Saskatchewan, to the northwest, and in eastern Ontario and Québec, to the southeast. However, there is no clear indication that a cold episode preceded the warming of the past 200 years in northwestern Ontario. The mean increase in ground surface temperature for all boreholes measured between 2000 and 2003 is 1.3 ± 0.8 K compared to 0.8 ± 0.6 K (σ) for the 1980 data. This difference suggests that the warming trend is persisting and might even have accelerated recently. Although the long‐term trend is climatic, part of the recent warming may be due to deforestation by the logging industry.
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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 | 0.001 | 0.001 |
| 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".