Accuracy, precision and quality control in age determination, including a review of the use and abuse of age validation methods
Bibliographic record
Abstract
Many calcified structures produce periodic growth increments useful for age determination at the annual or daily scale. However, age determination is invariably accompanied by various sources of error, some of which can have a serious effect on age‐structured calculations. This review highlights the best available methods for insuring ageing accuracy and quantifying ageing precision, whether in support of large‐scale production ageing or a small‐scale research project. Included in this review is a critical overview of methods used to initiate and pursue an accurate and controlled ageing program, including (but not limited to) validation of an ageing method. The distinction between validation of absolute age and increment periodicity is emphasized, as is the importance of determining the age of first increment formation. Based on an analysis of 372 papers reporting age validation since 1983, considerable progress has been made in age validation efforts in recent years. Nevertheless, several of the age validation methods which have been used routinely are of dubious value, particularly marginal increment analysis. The two major measures of precision, average percent error and coefficient of variation, are shown to be functionally equivalent, and a conversion factor relating the two is presented. Through use of quality control monitoring, ageing errors are readily detected and quantified; reference collections are the key to both quality control and reduction of costs. Although some level of random ageing error is unavoidable, such error can often be corrected after the fact using statistical (‘digital sharpening)’ methods.
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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 | 0.026 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".