Classifying Glare Intensity in Airborne Imagery Acquired during Marine Megafauna Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a classifier that takes airborne imagery acquired during marine megafauna surveys and classifies the glare intensity into four classes representing the severity of the glare. The objective of the classifier is to automate labour intensive and subjective components of the work performed by trained Marine Mammal Observers (MMOs). The proposed automatic method is based on a cascaded random forest architecture. The method uses features extracted from the histogram of the survey’s images and the metadata associated with respective images. The use of metadata is justified by the image formation model and we observed that it tends to improve the accuracy of the classifier. The proposed method provides results similar to that of trained MMOs. This is critical to the adoption of machine learning and machine vision technologies since introducing a change of methodology may impact the comparability of historic and future survey results when evaluating glare intensity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it