Suicide Prediction Analysis with Generalized Addictive Model
Bibliographic record
Abstract
The issue of suicide rate has become increasingly rigorous and has received extensive attention in the contemporary society. In order to explore the pattern of suicide rate variation and intrinsic incentive for suicide, the research was conducted to analyze the data involved multifaceted factors since the suicide is rarely caused by only one factor. Establishing statistical models like “Generalized Additive Model” provides a conceptual framework for the study and is instrumental in predicting the suicide rate in 2017. Based on the past data from year 1985 to 2016, the prediction of suicide rate in 2017 is presented specifically. Multiple invisible results are revealed including the negative linear relationship between GDP and suicide rate, the positive correlation with age and the uneven distribution worldwide. These results presented with functions and graphs show how financial, cultural or regional factors might affect the suicide rate in different areas. The prediction are still affected by factors difficult to estimate, such as socio-economic relations. However, the results are sufficient to help understand the suicide issue by analyzing it's origin and distribution.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".