Bibliographic record
Abstract
Over time, thermally protected and thermally insulated rail tank-cars may develop insulation deficiencies due to the continuous motion and vibrations. These deficiencies are generally not visible due to the protective outer steel jacket. A research program was undertaken to develop an inspection procedure to identify deficiencies in the thermal insulation. Thermography was selected as the most effective means of inspecting the thermal insulation because it is nondestructive, noncontact, and economical. Thermography takes advantage of the fact that when a temperature difference exits between the contents of the tank car and the ambient conditions, the presence of insulation deficiencies generates temperature gradients on the surface of the tank-car’s outer steel jacket that can then be identified using a thermal imager. A series of laboratory and field tests were conducted to determine under what ambient and tank conditions the inspection procedure is effective. Using a low-cost, uncooled, 8–12-μm waveband thermal imager, it was found that the imager could detect insulation deficiencies under temperature gradient conditions compatible with typical day-night cycle temperature variations. Field tests proved the technique to be practical and also showed that solar heating enhances the inspection procedure under certain conditions. This paper presents the results of the laboratory tests and shows some preliminary field test results. [S0094-9930(00)01104-5]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".