Mapping piping plover (<i>Charadrius melodus melodus</i>) habitat in coastal areas using airborne lidar data
Bibliographic record
Abstract
Coastal estuaries and beach habitat are some of the most important and productive ecosystems in Atlantic Canada. These sensitive areas are crucial for hundreds of land and marine species. Mapping and monitoring coastal habitat is important for the protection of species such as the endangered piping plover (Charadrius melodus melodus). Light detection and ranging (lidar) elevation and intensity data have been used together to successfully classify land-cover types. This study explores the use of elevation, texture, slope, and intensity to classify coastal habitat. Lidar data were collected over a barrier beach and estuary on the South Shore of Nova Scotia. Ground validation and training sites were collected using a real-time kinematic global positioning system. Unsupervised, supervised, and logical filter classifications were compared for separability of various beach and intertidal habitats. Coastal land classes similar in elevation, texture, and slope, such as mudflats, sand beaches, and salt marshes, relied heavily on intensity data for separation. Tidal saturation of these areas produced similar intensity returns, resulting in poor separation between classes. Logical filters applied to the lidar data improved the classification of coastal habitat compared to standard unsupervised and supervised classifications. Additional logical filters were used to isolate important nesting and feeding habitat for piping plover. Results of this study suggest that lidar can effectively be used for classifying coastal habitat if tidal and seasonal factors are taken into consideration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".