Bibliographic record
Abstract
History Engineering studies of freshwater ice began in the mid nineteenth century in eastern Europe. The flooding of Buda and Pest in 1838 led to studies of ice conditions on the River Danube during the winters of 1847/48 and 1848/49 by Arenstein (1849). Ashton (1986) and Barnes (1906) note that there were many nineteenth century studies of ice formation and ice jams. Ireland (1792) mentions “ground ice” rising up from the bottom of the River Thames and there were other eighteenth century references to this in France and Germany. Farquharson (1835, 1841) reports on anchor ice (ground-gru) observed in Lincolnshire, England, and proposed a theory of radiational cooling of rocks and vegetation in the river bed. Barnes (1906) published a study of frazil and anchor ice formation based on earlier literature and observations on the St. Lawrence River in Canada. Frazil is a French-Canadian term first used in 1831; anchor ice was originally termed ground ice (in Germany). Dunble (1860) studied the effects of lake ice on a 4-km-long railway bridge over Rice Lake, Ontario. Adams (1992) reports that Dunble (1860, p. 423) performed an experiment to demonstrate that “with the same change in temperature, the expansion and contraction of ice are equal”.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".