Comparison of Pennatula aculeata sea pen abundance at a fixed site over an extended period using machine learning, image annotation, and image cataloguing
Bibliographic record
Abstract
A benthic platform documented retraction behavior of Pennatula aculeata colonies in The Gully, Nova Scotia, Canada. The fixed site had 12,054 images taken every 30 min between October 2022 and July 2023. Three methods were used to quantify visible sea pens. A machine learning model using RootPainter was trained on cropped images of several colonies, with the areas classified as sea pens serving as a proxy for presences. The BIIGLE online platform was used to annotate one image per week, with colonies ranging from 10 to 52 per image. Finally, a catalogue with Adobe Lightroom was used to label 22 P. aculeata colonies individually, with 2 to 21 visible per cropped image. Similar values indicating presences over the months were produced across the three methods. Cataloguing required the most effort, though it was estimated to be faster than BIIGLE annotation and easier to produce counts than with RootPainter. Overall, RootPainter was preferred for its efficiency and Lightroom for its utility, while BIIGLE was not recommended because of the higher manual workload.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".