The foundations of the Patagonian icefields
Bibliographic record
Abstract
Abstract The two vast Patagonian icefields are a global hotspot for ice-loss. However, not much is known about the total ice volume they store - let alone its spatial distribution. One reason is that the abundant record of direct thickness measurements has never been systematically exploited. Here, this record is combined with remotely-sensed information on past ice thickness mapped from glacier retreat. Both datasets are incorporated in a state-of-the-art, mass-conservation approach to produce a well-informed map of the basal topography beneath the icefields. Its major asset is the reliability increase of thicknesses values along the many marine- and lake-terminating glaciers. For these, frontal ice-discharge is notably lower than previously reported. This finding implies that direct climatic control was more influential for past ice loss. We redact a total volume for both icefields in 2000 of 5351 km 3 . Despite the wealth of observations used in this assessment, relative volume uncertainties remain elevated.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".