Bibliographic record
Abstract
Incidents of venomous snake bites and their deleterious effects have been well documented in North American newspapers. In one year, worldwide, an estimated 300,000 people are bitten by snakes (Roberts, 1987). The number of deaths per annum attributed to snake bite is estimated at 50,000-100,000 (“Deadly viper, ” 2000). (See Appendix A). Although the total number of bites in Canada and the United States is lower than that of other countries, a number of fatalities resulting from envenomation have occurred. It has been estimated that 8,000 bites from venomous snakes occur each year in the United States (Dart & Gomez, 1996). In August of 1992, Larry Moor of Langley, British Columbia died shortly after a bite from an Egyptian cobra. Of particular interest is the fact that Mr. Moor was dedicated to educating the public about snakes. He was the founder of the B.C. Association of Reptile Owners and used to visit schools to correct children’s misconceptions about snakes and to teach them about their proper handling. (“Cobra bite, ” 1992; “Snake handler,”1992). Expertise in the area of venomous snakes does not ensure protection against a potentially fatal snake bite. More evidence for this comes from the case of Brian Leslie West from Emmitsburg, Maryland. Mr. West instructed local paramedics in the treatment of snake bite. However, in May
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".