Bibliographic record
Abstract
"Huffing," the form of substance abuse involving inhalants, is growing in popularity because of the ease and availability of chemical inhalants in many household products. The purpose in huffing is to achieve euphoria when the chemicals in question interact with the central nervous system in combination with oxygen displacement. The abuser is lulled into a false sense of safety despite the well-documented potential for lethal cardiac arrhythmia and the effects of chronic inhalant abuse, including multisystem organ failure, and brain damage. Huffing air conditioner fluid is a growing problem given the accessibility to outdoor units and their fluid components, such as difluorochloromethane(chlorodifluoromethane, Freon), and we have classified multiple cases of accidental death due to the toxicity of difluorochloromethane. Given the ubiquity of these devices and the vast lack of gating or security devices, they make an inviting target for inhalant abusers. Acute huffing fatalities have distinct findings that are present at the scene, given the position of the decedent and proximity to the air conditioner unit. The purpose of the autopsy in these cases is to exclude other potential causes of death and to procure specimens for toxicological analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".