Using UAVs in seabird research & monitoring: workshop at the 14th International Seabird Group Conference 2018
Bibliographic record
Abstract
Using UAVs in seabird research & monitoring: workshop at the 14th International Seabird Group Conference 2018 Organisers: Matt Wood (University of Gloucestershire) & Matty Murphy (Natural Resources Wales) Workshop outline: Rapidly advancing drone technology offers opportunities and challenges to both seabird researchers and the relevant regulatory bodies. This workshop will bring together experts in the practise and policy of using UAVs for seabirds, to share best practise on the design of UAV surveys to capture useful imagery whilst minimising disturbance. We aim to combine our experience with UAVs, to guide their future use and inform the development of policy. Participants will gain: - Insights on how to carry out an effective UAV survey whilst minimising disturbance to seabirds - Understanding of the regulatory framework that UAV users must follow - Opportunity to feed into a review of best practice in the use of UAVs for seabird surveys Time: 1400-1530 (1730, Monday 3 September 2018 Location: Central Teaching Hub, University of Liverpool Programme: Case studies of the use of UAVs with seabirds: Start: Matt Wood (University of Gloucestershire) Intro & Welcome 3min 45s: Norm Ratcliffe (BAS) A seabirder’s guide to UAVs 31min: Matty Murphy (NRW) UAVs and environmental law 46min: Robin Ward (NIRAS) Drone-captured still imagery to survey cliff-nesting seabirds at Flamborough & Filey pSPA 1h 03min: Graham Rush, Megan Stone, Lucy Clarke & Matt Wood (University of Gloucestershire). Semi-automated counts of breeding gulls 1h 18min 30s: Alex Banks & Alex Kilcoyne (Natural England). The use of UAVs to count inland breeding Lesser Black-backed Gulls in the Forest of Bowland 1h 33min 45s: Andy Webb (HiDef Aerial Surveying) Census of nesting common gulls in Aberdeenshire 1h 43min 50s: Kyle Elliot (McGill University). The use of UAV’s to count cliff-nesting seabirds in North America Recordings of presentations: Part 1: https://youtu.be/RmkBt4MLYdY Part 2: https://youtu.be/pbVB0_PiMvE (last 7 minutes of Kyle Elliot's presentation) A Seabirder's Guide to UAVs (Norm Ratcliffe, 25 mins) https://youtu.be/FEx0fcCKszM
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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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.444 | 0.844 |
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; both teacher heads agree on what is shown here.
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".