Bibliographic record
Abstract
Objectives of this chapter In this chapter we show how borehole temperature profiles can be used to infer past climate variations, and discuss the usefulness and limits of such methods. We also discuss the thermal conditions in ice sheets and show the importance of boundary conditions to calculate temperature profiles in the ice. The record of past climate in temperature profiles Time variations of the boundary condition at the Earth's surface affect subsurface temperatures with two important consequences: (1) the perturbations to steady-state temperature profiles may systematically affect the heat flux estimates, particularly in regions that were glaciated; (2) with careful measurements, these perturbations can be detected and interpreted to infer past variations in the surface boundary conditions. As early as 1923, temperature profiles from deep holes in the United States were used to infer the timing of the glacial retreat 10,000 years ago. The first heat flux estimates from Great Britain were corrected to account for the effect of the last glaciation. Birch (1948) proposed adjustments to account for the effect of the last glaciation on heat flux estimates in previously glaciated areas. The main obstacle for such corrections is that we still do not know what the temperature was at the base of the ice sheets. When the glacial retreat started, the temperature in the bedrock beneath the glacier was not in equilibrium. Although warming after the last glacial retreat 10,000 years ago is the dominating component of the temperature perturbation, the entire history of glacial retreats and advances must be included to calculate present perturbations to the temperature profiles (Figure 12.1).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.006 |
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".