Bibliographic record
Abstract
errors are rare. The diminutive “gletscherli” (small glacier) on Mount Titlis in Switzerland is only 80 m long (not 800 m as reported). The Krakatoa volcano erupted in 1883, not 1783, although there were two important volcanic eruptions in 1783 that may be identified in ice cores, which the authors describe. Two pictures of Mount Logan, Canada’s highest glaciated mountain, are impressive; however, it is approxi-mately 5959 m high, not 6050 m (an incorrect value commonly in use). On p. 188 Nevado Huandoy is misspelled, as is the name of the pro-glacial Lake Parón, a good map of which may be found in Ricker (1977). There is a generally well-prepared glossary of terms used in the text. The absence of equations has led to the (unavoidably) loose definition of the term strain as being “the amount by which an object—in this case glacial ice—becomes deformed under the influence of stress ” (stress is not formally defined in the glossary, but may be deduced adequately from the text). This point is not raised in criticism but only because it defines the book’s technical upper limit, to which the authors have paid very careful attention. Overall, this attractively priced book is both a very enjoyable read and a superbly illustrated glacier “travelogue. ” I highly recommend it to all those inter-ested in glaciers, professional glaciologists included.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.476 | 0.559 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".