Bibliographic record
Abstract
Comparison of official life tables construction in selected countries Abstract The main goal of this work is to analyze the methods used by the selected statistical offices in the construction of mortality tables. This work explored fundamental differences regarding the type of published life tables and parameters estimation methods. The theoretical part provides an overview of basic methodological information and description of methodology used, which was acquired during communicating with the selected statistical offices. During the analysis it was found that the differences in calculating the various functions of life tables are minimal, so the analytical part is mainly devoted to methods of estimating the probability of death at age 0 and smoothing of probability of death in older age. Acquired procedures and methods were applied to the data for the Czech Republic for year 2010, which allowed the comparison itself. The final part is the overall evaluation of achieved results, where can be also found commentaries on selected procedures and methods. The analysis shows that the most widely used type is a detailed cross-sectional life table. The most appropriate models of smoothing mortality curve are Kannistö-Thatcher (UK) Martinellův model (Sweden) and Kannistö (Canada). On the other side, the least...
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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 | 0.018 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".