Measurement Error in Fish Lengths: Evaluation and Management Implications
Bibliographic record
Abstract
ABSTRACT A fundamental aspect of fisheries science is measuring body length. Humans are inherently prone to error despite systems and provisions made to reduce it. We evaluated length measurement error (herein, referred to as “error”) and digit preference from fish studies conducted on the Colorado River and Little Colorado River in Arizona. Empirical error estimates varied among fish species and generally increased with fish size. We identified a digit preference for numbers ending in zero and five, which was exacerbated with larger sizes. Error effects on growth estimates were largest for fish recaptured after a short time, and we suggest guarding against the error phenomenon by removing data from fish captured and recaptured within a minimum of 30 days. Human, situation, and specimen induced error factors are described. Fisheries professionals should be cognizant of error factors, especially in situations when high precision and accuracy are required and results have important management implications. RESUMEN un aspecto fundamental en las ciencias pesqueras es la medición de la talla corporal. Los humanos somos inherentemente propensos a cometer errores pese a los sistemas y medidas preventivas que se utilizan para reducirlos. En este trabajo se evalúa el error asociado a la medición de la talla (en lo sucesivo se le llamará “error”) y la preferencia en el número de dígitos en los estudios ícticos llevados a cabo en el Río Colorado y el Río Coloradito, Arizona. Los estimados empíricos del error variaron entre especies de peces y en general se incrementaron conforme la aumenta la talla de los peces. Se identificaron preferencias en cuanto al número de dígitos para los números con terminación cero y cinco, lo cual se amplificó en los peces más grandes. Los efectos del error en las estimaciones de crecimiento fueron más grandes en el caso de los peces recién recapturados. Se sugiere tratar el fenómeno del error mediante la remoción de los datos provenientes de peces recapturados en los primeros 30 días después de su liberación. Se describen los factores de error humano, de medición y asociado al espécimen. Los profesionales de las pesquerías deben ser conscientes de los factores de error, especialmente en situaciones en las que se requieren precisión y exactitud y cuando hay implicaciones importantes para el manejo.
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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 | 0.022 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".