Machine Learning Quantifies Fine‐Scale Hairiness in Shore Flies (Diptera: Ephydridae)
Bibliographic record
Abstract
Morphological analysis of fine structures on small insects is often labor intensive, scale-limited, and biased by sampling or organismal life history. We used a pixel classification machine-learning workflow with the open source programs Ilastik and Fiji to identify and quantify microtrichia in semiaquatic shore flies (Ephydridae). This methodology semi-automates quantification of hairs by counting objects or groups of class-assigned pixels and determining their percent coverage at a given magnification using scanning electron micrographs. Our results are consistent with manual counts, with Paracoenia species that tolerate hot springs having more hairs than less aquatic Parydra. However, Paracoenia hairs tend to be shorter, and the percent coverage of microtrichia per unit surface area did not differentiate species except for the anterior thoracic spiracle. Our workflow is adaptable for use in other taxonomic groups or beyond the quantification of hairs, with the upper limits of applicability determined by overlap in the feature of interest. As molecular datasets continue to grow and proliferate in the multi-omics age, efficient morphological workflows become even more critical to allowing proportionally robust, complementary biological inferences grounded in phenotypic data.
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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) | 0.001 | 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 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".