A Comparative Analysis of Rubble Field Data Collection Techniques
Bibliographic record
Abstract
The physical characteristics of a grounded rubble field can be difficult to evaluate. This may appear to be of little consequence at first; however these characteristics play a key role in determining the stability and loading absorbed by the rubble field. In the past, one of the most effective methods of collecting physical data from a rubble field was to perform a survey on the ice and use physical observations to determine the characteristics of a given field. However, with the advent of more sophisticated technology and observation equipment, manually surveying these formations may no longer be as frequently required. Data were obtained during the spring of 2010, at the rubble field that formed at the Minuk I-53 remnant exploration drill site in the western Canadian Beaufort Sea. This paper compares the quantitative results obtained from three methods: An on-ice survey, video and laser altimeter data collected from a helicopter and a digital elevation model (DEM) created from stereo satellite imagery of the rubble field. The paper examines their respective advantages and disadvantages with respect to obtaining roughness characteristics of a rubble field. The on-ice survey produces the most reliable results, however it is time-consuming and costly. The video and laser altimeter system provided a high volume of data, which correlated well with the on-ice survey, however its accuracy needs refinements for use with extensively ridged and rubbled regions of ice. The DEM did not correlate particularly well with the on-ice survey, but this too could be improved upon with further detailed examinations of its control points and matching features with what was observed in the field.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".