UAV Imagery - 2016 - Coastal British Columbia - Canada
Bibliographic record
Abstract
This is a dataset of UAV imagery collected and processed by the Hakai Institute. In 2016 unmanned aerial vehicles at the Hakai Institute covered a range of areas along the British Columbia Coast. Here we list the locations of our projects - further metadata on each of these locations is available upon request. The purpose of this record is to highlight areas of imagery that have been covered and aid researchers in locating available imagery. Restrictions on imagery distribution exist only in areas of cultural sensitivity. Imagery is typically provided in georeferenced TIFF format. RAW / JPEG images are also available. In some cases digital surface models have been created along with these imagery. Spatial resolutions vary from 1 cm to 12 cm depending on flight elevation. Please use the links section below to observe an interactive map detailing the locations of our imagery. Metadata attributes recording flight log and processing notes include: MOBE_ID (unique flight mission identification number), date, location, size of area covered (grid size), pilot, weather, project (subject), crew, tide (meters), UAV used, elevation flown, UAV application used for flight, # of flights conducted for the mission, total flight time (minutes), flight notes, file location, processing status, storage location, and processing notes. Please contact data@hakai.org for more information. UAV equipment for 2016: Phantom 2 and Phantom 3. All flights in 2016 have been conducted by Derek Heathfield, Luba Reshitnyk, Will McInnes, and Keith Holmes. Software for data processing: PIX4D, Autostitch, and ArcGIS.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.019 |
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".