Bibliographic record
Abstract
The acoustic emissions from rubbing the ends of baseball bats, thin wood rods, and the soles of rubber boots on a snow bed were recorded and analyzed. The same analysis was also extended to the acoustic emissions from impacting a snow bed by small pestles and by subjecting a snow bed to relatively large stresses by stepping on it with shoes of variable rigidity. It is shown that when a snow bed is lightly rubbed, the acoustic emissions originate from vibration mode excitation in the rubbing body. It is argued that when highly granular cold snow is impacted by a small pestle, the acoustic emissions could originate with mode excitation in granule vibration bands around the pestle end, as in the case of impacted singing sands. Layers of highly granular and rounded snow pellets, seemingly formed from frozen raindrops, could contribute especially to snow avalanches. When walking on a snow bed, the acoustic emissions include squeaky sounds that originate from mode excitation in the shoe sole and crunchy and squeally sounds that could originate with crack growth and crystal dislocation processes in the sheared matrix of snow grains and grain bonds.
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.
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 | 0.000 | 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) | 0.002 | 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".