Bibliographic record
Abstract
A modular ice impact panel, intended for future ship / bergy bit collision tests, has been designed that incorporates new pressure-sensing technology. A single module of the panel, with sensing area of 1 m2, has been fabricated and tested in the lab by dropping heavy masses of freshwater ice on it from heights up to 1.8 m. The central component of the Impact Module is a large cast acrylic block, 1 m x 1 m x 0.5 m, housed in a strong steel frame. The Impact Module design is made possible by the strength and transparency characteristics of the acrylic. The Module utilizes two types of pressure sensors, one consisting of strain-gauged acrylic cylinders (2.5 cm diameter) that are imbedded in the face of the acrylic block at 9 locations. The other technology is an optomechanical system consisting of many thin narrow strips (13 mm x 0.9 m x 4 mm) of acrylic that cover the impacting face of the acrylic block. Elastic flattening of the slightly convex bottom surface of the strips occurs when pressed against the face of the block during impacts. A highspeed video camera, positioned behind the transparent block, records the deformation of the strips from which the corresponding pressure is determined from calibrations. The effective squareshaped unit sensing area for this technology is 13 mm x 13 mm, implying that the Impact Module has the equivalent of about 4500 symmetric-shaped pressure sensors on its surface. The acrylic block is supported on four large flat-jack load cells that measure the impact loads. The tests demonstrated that the Impact Module is very robust and the pressure-sensing technologies yield consistent results. The large capacity load sensors were also shown to be functional.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".