A Perspective on Perspectives: Methods to Reduce Variation in Shape Analysis of Digital Images
Bibliographic record
Abstract
Abstract We present digital imaging methods for geometric morphometric analysis of shape, and we describe issues associated with improper image acquisition by using lake trout Salvelinus namaycush as an example. The choice of imaging equipment, the configuration of that equipment, and the orientation of the specimens with respect to the camera lens can lead to inaccurate imaging and ultimately to error in landmark placement during morphometric analysis. Lake trout that were imaged at 15‐mm focal length and 0.5‐m focal distance (treatment 1) were distorted in comparison with fish that were imaged at 50‐mm focal length and 2‐m focal distance (treatment 2). Deformation grids showed dramatic variation in the horizontal plane along the length of the fish, especially midbody, suggesting that barrel distortion was occurring at the 15‐mm focal length. Partial warp scores resulting from geometric analysis of body shape differed for all fish on all 18 warps as a result of the different focal length and distance treatments for image capture. To minimize perspective (orientation) and distortion (equipment) errors, we recommend using a digital single‐lens reflex camera (>5 megapixels) with a lens that has a focal length exceeding 35 mm, a horizontal tripod to position the lens directly over the specimen, a mesh cradle to create a planar imaging surface, and dissection pins to display the fish in a standard orientation. The method presented herein will aid in reducing measurement error associated with landmark homology and will promote comparability of geometric shape data among studies.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| 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".