Use of fixed-wing and multi-rotor unmanned aerial vehicles to map dynamic changes in a freshwater marsh
Bibliographic record
Abstract
We used a multi-rotor (Phantom 2 Vision+, DJI) and a fixed-wing (eBee, senseFly) unmanned aerial vehicle (UAV) to acquire high-spatial-resolution composite photos of an impounded freshwater marsh during late summer in 2014 and 2015. Dominant type and percent cover of three vegetation classes (submerged aquatic, floating or emergent vegetation) were identified and compared against field data collected in 176 (2 m × 2 m) quadrats during summer 2014. We also compared these data against the most recently available digital aerial true colour, high-resolution photographs provided by the government of Ontario (Southwestern Ontario Orthophotography Project (SWOOP), May 2010), which are free to researchers but taken every 5 years in leaf-off spring conditions. The eBee system produced the most effective data for determining percent cover of floating and emergent vegetation (58% and 64% overall accuracy, respectively). Both the eBee and the Phantom were comparable in their ability to determine dominant habitat types (moderate kappa agreement) and were superior to SWOOP in this respect (poor kappa agreement). UAVs can provide a time-sensitive, flexible, and affordable option to capture dynamic seasonal changes in wetlands, information that ecologists often require to study how species at risk use their habitat.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".