Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This dataset contains estimates of debris emergence elevations for 974 debris-covered glaciers > 5 km^2 in High Mountain Asia, calculated for three compositing periods: 1985-1999, 2000 - 2010, and 2013 - 2017. Composites were constructed in Google Earth Engine with Landsat imagery, with relatively cloud-free imagery and pixels that were preferentially selected for high brightness temperatures to remove biases due to cloud or snow cover. The elevation of the transition zone between ice and debris was calculated as the median elevation of pixels that for each debris-covered glacier in the Randolph Glacier Inventory (RGI V5) using Shuttle Radar Topography Mission (SRTM) elevation data and the boundary between ice and debris delineated in the composites. Also included in this dataset are the average date of acquisition and day of year of acquisition for individual glacier composites, individual glacier mass balance trends from 2000 - 2017 (m w.e./yr; Brun et al. 2017), changes in glacier velocity (Dehecq et al. 2019), and modelled estimates of future glacier change (Kraaijenbrink et al., 2017).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.019 | 0.002 |
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 it