A General Methodology for Use of Paint Brushes in Snow Profile Investigation
Bibliographic record
Abstract
The use of a brush was suggested by Anderson (1960) as an aid to highlight the separation of layers in the snowpack. Interpretation was briefly described in early U.S. Forest Service avalanche manuals (1961). Presently it is listed as an optional tool for snow profile studies in U.S. and Canadian recording standards (Greene et al. 2004, CAA 2002). During the winter of 2005/06 an analysis of key brush properties and performance characteristics was carried out using standardized tests. Field-testing was conducted on a selection of brushes of varying properties and characteristics for their effect in highlighting snow layering. A sufficiently general technique was developed and tried in the field that incorporated a relationship to the common hand hardness test. This poster presents a description of method, the results of the investigation, and brush selection recommendation for use in snow profiles. References Andersen, V.H. 1960. A technique for photographing snow-pit stratigraphy. Journal of Geophysical Research 65 (3):1080-1082. CAA. 2002. Observation Guidelines and Recording Standards for Weather, Snowpack and Avalanches. Revelstoke: Canadian Avalanche Association. Greene, E., K. Birkeland, Kelly Elder, G. Johnson, C. Landry, I. McCammon, M. Moore, D. Sharaf, C. Sterbenz, and K. Williams. 2004. Snow, Weather, and Avalanches: Observational Guidelines for Avalanche Programs in the United States. Pagosa Springs: American Avalanche Association. USFS. 1961. Snow avalanches: a handbook of forecasting and control measures, edited by U.S.D.A.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.020 |
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".