Ground surface paleotemperature reconstruction using information measures and empirical Bayes
Bibliographic record
Abstract
We outline an empirical Bayesian approach to ground‐surface temperature (GST) reconstruction that utilizes Akaike's Bayesian information criterion (ABIC). Typical unknown statistical quantities, such as the noise variance and so on, are automatically determined through the analysis. We compare the ABIC inversion to the singular value decomposition on a synthetic downhole temperature data set. In comparing the root mean square errors between the synthetic climatic signal and each of the reconstructions (singular value and ABIC) from 1900 to 2002, we see that the ABIC solution produced the ‘best’ reconstruction in a mean square sense. We also carry out an analysis of the Canadian borehole data set in which we use 221 temperature profiles. The reconstructed GST record shows warming between 1800 and 1949 of approximately 1.0 K, with the maximum rate of warming occurring between 1900 and 1949.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".