Jellyfish Monitoring UAV Imagery - Pruth Bay - Calvert Island - British Columbia - Canada
Bibliographic record
Abstract
UAV imagery of jellyfish blooms in Pruth Bay, Calvert Island, BC. Imagery is stitched and georeferenced in order to extract jellyfish numbers and physical orientation. Data collection and analysis is being conducted by the Hakai Institute with further research at the University of British Columbia under the supervision of Dr. Brian Hunt. In this study, we tested the application of UAVs to aerial surveys of jellyfish and their suitability for measuring and monitoring aggregations. We paired net hauls with linear image transects taken by a UAV to measure 5 Aurelia spp. aggregations over the course of 1 d in Pruth Bay, British Columbia, Canada. Georeferenced image transects were processed to determine aggregation areal extent and estimate percent cover of jellyfish. The percent cover estimates and net haul density data were highly comparable for all aggregations. Using combined UAV-derived surface area estimates and net haul biomass estimates, we calculated that jellyfish aggregation size ranged from 65 to 117 t wet weight biomass. We discuss the potential for additional UAV-based measurements including jellyfish abundance and individual size. The study demonstrates the potential of UAVs as powerful tools for characterizing and researching jellyfish aggregations in situ. Data is stored as 15 georeferenced datasets and is stored in the Hakai server - UAV Files - Calvert - September 11, 2016 Folder. All uav flights, data processing, mapping, and reporting conducted by Keith Holmes. Contact data@hakai.org for more information
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".