Bibliographic record
Abstract
For this project, we questioned if it was possible to use regression analysis to predict the average life expectancy of a country’s citizen. The world is concerned about health due to poor air quality, inadequate sanitation, and lack of healthy drinking water. We used regression analysis to analyze these variables to see if they contributed to the overall life expectancy of a nation. We selected beneficial variables and eliminated variables that were ineffective. Although there are some variables that may seem to be effective, after checking residuals and correlations, we concluded which variables are useful. We chose a small sample size of twenty countries randomly in order to try to predict an accurate model for life expectancy of any country. The countries selected were Iraq, Oman, Tonga, Spain, Mongolia, Samoa, Qatar, Pap New Guinea, Lesotho, Mali, Bulgaria, Trinidad and Tobago, Canada, Bangladesh, Tanzania, Micronesia, Mauritius, Suriname, Austria, and Sao Tome and Principe. With the research conducted, we will be able to show the inadequacies of nations that affect life expectancy, and how to increase the average life expectancy of their citizens. (Author abstract)
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".