Methodological improvements in uncertain classification of individual-level demographic measurements: Improving reliability of inferences from citizen-science and field data
Bibliographic record
Abstract
Abstract: Both professional and citizen-science field work often generate uncertain sample measurements. Definitive age and sex determinations can be notoriously difficult and/or costly to ascertain for specific individuals in the field, and even correct species identification can be problematic in citizen-science endeavours. While a variety of statistical techniques exist for quantifying such uncertainty at the population or sample level, when this uncertainty exists at the level of the individual datum, analysts are either forced to treat the unit-level information as definitive or discard it altogether. Either approach can introduce bias into subsequent sample estimates and necessarily mischaracterizes their associated measures of uncertainty. In this presentation, we will discuss random-variable-valued measurements, a new approach that we have developed to directly incorporate the sample-unit-level uncertainty of measurements into traditional data analysis, and introduce software for easy implementation. Our approach allows analysts to properly utilize field measurements on individuals that are both definitive and tentative (e.g. partial/uncertain sex or age measurements), improving the accuracy and reliability of resulting inferences that are crucial for the quantification of seabird population demographics. Authors: Edward Kroc¹, Louise Blight² ¹University of British Columbia, ²Procellaria Research & Consulting
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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 | 0.257 | 0.568 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".