Using unmanned aerial vehicles (UAVs) to measure jellyfish aggregations
Bibliographic record
Abstract
MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 591:29-36 (2018) - DOI: https://doi.org/10.3354/meps12414 NOTE Using unmanned aerial vehicles (UAVs) to measure jellyfish aggregations Jessica Schaub1,*, Brian P. V. Hunt1,2,3, Evgeny A. Pakhomov1,2,3, Keith Holmes3, Yuhao Lu4, Lucy Quayle3 1Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada 2Institute of the Oceans and Fisheries, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada 3Hakai Institute, PO Box 309, Heriot Bay, British Columbia V0P 1H0, Canada 4Integrated Remote Sensing Studio, Forest Sciences Center, 2424 Main Mall, Vancouver, British Columbia V6K 1Z4, Canada *Corresponding author: jessica.schaub@alumni.ubc.caAdvance View was available online January 29, 2018 ABSTRACT: Unmanned aerial vehicles (UAVs, or drones) are becoming increasingly common as tools to perform high-resolution but broad-scale measurements of habitats and populations simultaneously. 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. KEY WORDS: Drones · Jellyfish aggregations · GIS · Unmanned aerial vehicles Full text in pdf format Supplementary material PreviousNextCite this article as: Schaub J, Hunt BPV, Pakhomov EA, Holmes K, Lu Y, Quayle L (2018) Using unmanned aerial vehicles (UAVs) to measure jellyfish aggregations. Mar Ecol Prog Ser 591:29-36. https://doi.org/10.3354/meps12414 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 591. Online publication date: March 19, 2018 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2018 Inter-Research.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".