Bibliographic record
Abstract
Aim: To analyze the distribution and trends of incidence and death rates among males and females along with the period, using the historical lung cancer data of Australia. The purpose is that, with the analysis of the selected age-groups, it can generate some inspiration for lung cancer prevention, as an inch of prevention is better than a mile of treatment. Background: Cancer has become the second leading cause of morbidity and mortality around the world following the heart disease. Among the top five common cancers listed by the WHO, lung cancer has been the always No.1 cause of death among most countries. Although the lung cancer incidence rates in developed countries are relatively low (about 40 per 100,000 in average, among Australia, Canada, Denmark, England, Finland, France, and the United States), there are quite significant differences among different countries. It is of practical interest to study these in detail, and for that purpose, Australia, where in 2012, they smoked 21 Billion cigarettes, was chosen. Result: Generally speaking, lung cancer incidence in Australia presented a decreasing trend in last few decades and it will keep stable for next few decades. From gender point, male incidence rate is keep decreasing slowly and female rate began to decrease after twenty-year increasing. Among all new diagnosed cases, 60-year old people have an increased percentage than before.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".