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Record W7135606364

Comparison of official life tables construction in selected countries

2015· dissertation· cs· W7135606364 on OpenAlexaboutno aff
Ivan Godunov

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2015
Typedissertation
Languagecs
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsEstimationCzechWork (physics)SmoothingStatistical analysisTable (database)
DOInot available

Abstract

fetched live from OpenAlex

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...

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.309
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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