1-km resolution rebound surfaces and paleotopography of glaciated North America since the Last Glacial Maximum
Bibliographic record
Abstract
We present a series of 1-km spatial resolution rebound (isobase) surfaces based on publicly distributed predictions obtained from the glacio-isostatic adjustment models known as ICE-5G (VM2 L90), ICE-6G_C (VM5a) and ICE-7G_NA (VM7). Our objective is to provide readily accessible tools for a broad range of geological and paleoenvironmental studies, and to facilitate direct comparison between models' predictions and field-based observations. Rebound surfaces were interpolated at the scale of North American ice sheets (35.5°-89.5°N; 45°-165°W) and for each time increment of the models (1,000-500 yrs, between 26,000-21,000 yrs BP and present-day). The assessment of the interpolations indicates that the rebound surfaces have an overall vertical accuracy of ∼0.4 m compared to original ICE-xG outputs. These rebound surfaces were combined with the GEBCO 2021 present-day elevation grid to reconstruct the paleotopography for each time increment of the models and are all presented as raster files that can be easily integrated into geographical information systems. The resulting datasets therefore provide a unique support for geological, paleoenvironmental and archeological studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 0.001 |
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".