Selection of borehole temperature depth profiles for regional climate reconstructions
Bibliographic record
Abstract
Abstract. Borehole temperature depth profiles are commonly used to infer time variations in the ground surface temperature on centennial time scales. We compare different procedures to obtain a regional ground surface temperature history (GSTH) from an ensemble of borehole temperature depth profiles. We address in particular the question of selecting profiles that are not contaminated by non climatic surface perturbations and we compare the joint inversion of all the profiles with the average of individual inversions. Very few profiles of the Canadian data set meet the selection criteria (e.g. only 13 out of 73 profiles in Manitoba and Saskatchewan were retained). We show that the resolution and the stability of the inversion of selected profiles are much improved over those for a complete data set. When profiles have been selected, the average GSTH of individual inversions and the GSTH of the joint inversion are almost identical. This is not observed when the entire data set is inverted: the average of individual inversions is different from the joint inversion. We also show that the joint inversion of very noisy data sets does not improve the resolution but, on the contrary, causes strong instabilities in the inversion. When the profiles that are affected by noise can not be eliminated, averaging of the individual inversions yields the most stable result, but with very poor resolution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".