Glacier changes on the Tibetan Plateau derived from Landsat imagery: mid-1970s – 2000–13
Bibliographic record
Abstract
ABSTRACT Glacier area changes on the Tibetan Plateau were studied in different drainage basins based on Landsat satellite images from three epochs: 263 in the mid-1970s, 150 in 1999–2002 and 148 in 2013/14. Three mosaics (M1976, M2001 and M2013) with minimal cloud and snow cover were constructed, and the uncertainty due to each epoch having a finite span was accounted for. Glacier outlines (TPG1976, TPG2001 and TPG2013) were digitized manually with guidance from the SRTM DEM v4.1 and Google Earth imagery. To achieve complete multi-temporal coverage in a reasonable time, only debris-free ice was delineated. Area mapping uncertainty was evaluated at three study sites, Mount Qomolangma (Everest), Mount Naimona'Nyi, Mount Geladandong, where the largest differences between present and earlier measurements were within ~±4%. Area differences with previous inventories ranged from −19.6% (TPG1976 minus the first Chinese Glacier Inventory) to −3.6% and −1.1% (TPG2013 and TPG2001, respectively minus the second Chinese Glacier Inventory), while the difference TPG2001 minus the GAMDAM Glacier Inventory was +10.4%. Glacier area on the plateau decreased from 44 366 ± 2827 km 2 (1.7% of the study area) in the 1970s to 42 210 ± 1621 km 2 in 2001 and 41 137 ± 1616 km 2 in 2013. Shrinkage was faster in external drainage basins of the southeast than in the interior basins of the northwest, from a maximum of −0.43% a −1 (−1.60% a −1 during 1994–2013) in the Mekong catchment down to a minimum of −0.12% a −1 in the Tarim interior drainage.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".