Accuracy, precision and quality control in age determination, including a review of the use and abuse of age validation methods
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A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.267 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
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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The record
- Venue
- Journal of Fish Biology
- Topic
- Insurance, Mortality, Demography, Risk Management
- Field
- Social Sciences
- Canadian institutions
- Bedford Institute of Oceanography
- Funders
- —
- Keywords
- AgeingScale (ratio)StatisticsComputer scienceQuality (philosophy)MathematicsBiology
- Has abstract in OpenAlex
- yes