North American climate of the last millennium: Underground temperatures and model comparison
Bibliographic record
Abstract
General circulation models (GCMs) are currently able to provide physically consistent simulations of millennial climate variability in which estimations of external forcing factors are incorporated as boundary conditions. Climate reconstruction attempts to recover as faithfully as possible past climate variability using a variety of independent and climate‐sensitive sources of information. By deriving strategies of comparison between GCM simulations and proxy data, or directly recorded data such as subsurface thermal profiles, the agreement between model and observations can be assessed. Thermal profiles obtained from the boreholes of North America were grouped into eight geographically discrete ensembles and averaged to form robust, representative profiles. The gridded output from the three distinct integrations of the GCM ECHO‐g were similarly averaged by region. These simulated, millennial, paleoclimatic histories were then forward modeled to arrive at the subsurface thermal profiles that would result from the temperature trends at the surface. These forward modeled profiles were then compared with the borehole average thermal anomaly profile in each region. In most of the regions studied, the externally forced runs from ECHO‐g are in better agreement with underground temperature anomalies than with the control run, suggesting that boreholes are sensitive to external forcing. Not only do ECHO‐g simulations demonstrate better agreement with borehole data when considering variable external forcing factors, but ECHO‐g also appears to broadly describe qualitative aspects of long‐term climatic trends at a regional scale.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".