Defining exposure time using burn severity of skin tissue under the scanning electron microscope
Bibliographic record
Abstract
This research examines whether a scientist can determine the time interval associated with skin when exposed to a burning substance using observation through an Environmental Scanning Electron Microscope (ESEM). The microscope barrages the surface of the skin with electrons, giving the viewer an image of the topography of the sample. Pig epithelial tissue was the medium experimented upon due to its similarity to human skin and was stored in formaldehyde to keep the tissue fresh until examination could occur. The tissues were burned chemically (with concentrated and half-diluted hydrochloric acid) as well as thermally (using a household non-industrial refrigerator/freezer unit and a toaster oven) with controlled variables allowing only exposure time and percentage of fat to be variable within the experiment. Determining the exposure time interval betters the scientific understanding of pin-pointing evidence leading to a near-definitive estimation of timed exposure. When burning the substance thermally, large bubbles and tearing were seen in the frozen tissue whereas many small bubbles were seen dotting the heated tissue. In terms of the acid, heavy pocketing occurred in the skin, eating the epithelial layer away from the sample. Thus, the results show that as the time interval of exposure increases, the observed artifacts of burning (bubbling and pocketing) also increase in number. In summary, due to the results collected in this experiment, the time interval in which the skin was exposed to a burning substance can be approximated based on the number of artifacts seen on the skin under the ESEM.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".