Panoramic image set and deep learning model for monitoring Double-crested Cormorant nesting on the Ironworkers Memorial Second Narrows Bridge in Vancouver, British Columbia, Canada.
Bibliographic record
Abstract
This dataset contains high-resolution panoramic images and a trained deep learning model developed for monitoring Double-crested Cormorant nesting activity on the Ironworkers Memorial Second Narrows Bridge in Vancouver, British Columbia, Canada. Weekly images were collected during the 2020 and 2021 breeding seasons using a Sony α7R IV camera mounted on a Gigapan robotic system. The images were stitched into large-scale panoramas using PTGui Pro software. A subset of these images was annotated and used to train a TensorFlow 2 object detection model capable of identifying individual birds and nests in various positions and stages. The dataset includes model configuration files, checkpoints, and the final trained model in SavedModel format, as well as eight stitched TIFF panoramas from the 2020 season used for validation. This dataset supports the development and evaluation of automated workflows for avian colony monitoring and contributes to research in ecological computer vision.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".