The International Database on Longevity: Data Resource Profile
Bibliographic record
Abstract
Abstract Even in countries with very good statistical systems, routine population statistics that cover individuals of very high ages are often problematic, as the proportion of erroneous cases increases sharply with age. The desire to measure human mortality at extreme ages was the main motivation for the establishment of the International Database on Longevity (IDL). The IDL is a uniquely valuable source of information on extreme human longevity. It provides high-quality age-validated individual-level data on the ages of semi-supercentenarians and supercentenarians. Moreover, the IDL is the only database that provides such data without age-ascertainment bias. It obtains its candidates from records of government agencies to ensure that there is no dependency between the probability of being included and age. Candidates who meet strict criteria for the validity of their age (date of their birth) are then included in the IDL. Nevertheless, the IDL does not include exhaustive sets of validated supercentenarians and semi-supercentenarians for any country, because it is nearly impossible to find documents that would allow for the validation of the ages of all of the individuals on the list. As of August 2017, the IDL has records on 1,304 validated supercentenarians and 18,590 semi-supercentenarians from 15 countries. The first person in the IDL collection who attained age 110 was born in 1852 and died in 1962 in Quebec, while the last person was born in 1906 and attained age 110 in 2016. This chapter introduces the database and explains its purpose and principles. We also describe the data structure and provide an overview of the information available.
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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.013 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.142 | 0.113 |
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".