Comparison of Specimen- and Image-Based Morphometrics for Cisco
Bibliographic record
Abstract
Abstract Morphometric data from fish are typically generated using one of two methods: direct measurements made on a specimen or extraction of distances from a digital picture. We compared data on 12 morphometrics collected with these two methods on the same collection of Cisco Coregonus artedi from Lake Ontario, North America, to assess the degree of bias in measurements made directly on a specimen- vs. an image-based method. We also assessed the degree of reproducibility within the image-based method by evaluating the amount of variation between different analysts for each morphometric method. Our results indicate specific morphometrics may be more prone to bias across the two methods and between analysts. Four of 12 morphometrics evaluated showed significant deviation from a 1:1 relationship that would be expected if the imaged-based method produced accurate specimen-based measurements. Pelvic fin length and pelvic–anal fin distance had the highest between-analyst variation for image-based landmarks, indicating low reproducibility for these metrics, compared with pectoral fin or total length, which had lower between-analyst variation. Although some morphometric measurements can be accurately obtained with either method, and therefore potentially used interchangeably in studies on Cisco morphology, our findings highlight the importance of considering method bias in morphometric studies that use data collected by different methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".