Exploring the Relationship Between Garlic Intake and the Risk of Getting Different Types of Cancer
Bibliographic record
Abstract
Whether the correlation exists between the risk of getting cancer and garlic intake is a mystery. On one hand, the media advertise that garlic could lower the opportunity of getting cancer, but on the other hand, no serious proof or studies are given. In order to quantitatively demonstrate the authenticity, a meta-analysis is applied grounded upon previous works for exploring the relationship between garlic and cancer. Five different types of cancers are considered in this work, including gastric, colon, prostate, breast, and lung cancer. The collected dataset is analyzed by the linear probing model for evaluating the previous results measured by risk ratio (RR) or odds ratio (OR). Then the averaged correlations between garlic intake and cancer of different types are calculated and further visualized for comparison. The results show that no major correlation is identified between garlic intake and the opportunities of suffering from all kinds of cancer. Moreover, the degree of correlation between different types of cancer can have a discrepancy to some extent.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".