Spam Detection: A Syntax and Semantic-Based Approach
Bibliographic record
Abstract
In a household health survey more than 15 000 individuals in four areas of Canada were interviewed as part of the World Health Organization/International Collaborative Study of Medical Care Utilization. Data were collected to describe the health services system in each area and to measure the population's utilization of health professionals, hospitals, medicines and selected preventive services, perceived acute and chronic morbidity, attitudes and beliefs about health and health care, and sociodemographic characteristics. The proportion of persons with perceived morbidity was twice that of persons reporting visits with a physician in the same 2-week period. Prescribed and nonprescribed medications had been used by more than 50% of respondents in each area in the 2 days before the interview, nonprescribed medicines accounting for more than half of this use. Respondents were found to be more sceptical of medical doctors than of medical science.
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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 | 0.000 | 0.000 |
| 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".