Time series of surface kelp canopy area derived from remotely piloted aerial systems (RPAS, or drone) surveys, Central Coast, British Columbia
Bibliographic record
Abstract
This data package represents a time series of canopy area of giant kelp, Macrocystis pyrifera, and bull kelp, Nereocystis luetkeana, derived from remotely piloted aerial system (RPAS or drones) surveys, along with relevant metadata. The kelp canopy is composed of the portions of fronds, stipes and blades floating on the surface of the water. RPAS surveys are conducted annually at long-term monitoring sites surveyed by the Hakai Institute on the Central Coast of British Columbia, Canada. These data are collected as part of the Hakai Institute Habitat Mapping Program whose broader goal is to document and understand long-term trends of kelp forests dynamics and drivers at local, regional and coast-wide scales. The Hakai Institute started using drones in 2015 as part of this work in order to capture site-level data on kelp forest distribution for long-term ecological research. Drone surveys are conducted annually in July/August during low tide (<1.5 m, chart datum) and collect RGB (red-green-blue) imagery. Canopy area is derived from drone-derived orthomosaics using a machine learning tool, the Kelp-O-Matic, which automates the detection of extent of kelp canopy area present in high-resolution orthomosaics. Areal data are classified to species level. Outputs are reviewed by a trained analyst. Canopy area (m2) data are provided as vector features (shapefiles) in NAD83 UTM Zone 9 clipped to each site area of interest (AOI) to ensure the same areas are compared over time and then published to a geodatabase. This data package includes a geodatabase which includes: - Polygon vector features of canopy kelp - Polygon vector features of the area of interest (AOI) of each monitoring site - A metadata report (.pdf) which describes methods for imagery collection, generating orthomosaics and delineating kelp extent. - Data dictionary (spreadsheet) which describes the attributes of the polygon vector features for canopy kelp. This data package is freely available to everyone, following the principles of equitable access and benefit sharing. However, we expect all data users to give attribution to the data providers (read our data license) and the use of these data should happen in the light of fair use, i.e.: 1) respect the data providers, and provide helpful feedback on data quality, and 2) communicate and/or collaborate with the providers if you are considering using this dataset for manuscripts or other forms of reporting.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".