Spatial extent of surface canopy kelp derived from fixed-wing surveys (2024), North Vancouver Island, British Columbia, Canada
Bibliographic record
Abstract
This data package consists of digitized surface canopy extent for two species of canopy-forming kelp – giant kelp (Macrocystis pyrifera) and bull kelp (Nereocystis luetkeana) – for two regions on the North Vancouver Island region of British Columbia, Canada. These data were collected in partnership between Marine Plan Partnership for the North Pacific Coast (MaPP) and the Hakai Institute as part of the MaPP Regional Kelp Monitoring Program (RKMP). Spatial datasets of kelp canopy were derived from high resolution aerial imagery (10 cm) collected from fixed-wing aerial surveys flown on September 20th, 2024. The survey regions included (1) Heydon, (2) Hickey Point/Sayward area, (3) Klaoitsis region and (4) Port Neville/Hanatsa Point region. Survey windows for imagery collection were considered based on obtaining imagery during maximum kelp extent and during a low tide cycle therefore survey windows were planned between July through September during tides of less than +2.0 m (mean low low water). Imagery Capture: Four-band imagery (red-green-blue-near infrared) was collected using two high resolution cameras aboard the Hakai Institute Aerial Coastal Observatory (a fixed-wing aircraft). Imagery resolution is 10 cm. Imagery collection and orthomosaic processing is described in associated reports linked in this record. Kelp delineation: Surface kelp canopy (species-level) is represented by polygons which were digitized using the Hakai Institute KelpOMatic AI tool (https://habitat-mapper.readthedocs.io/en/latest/). Datasets were reviewed by an expert analyst for quality analysis/quality control purposes. The data package includes: - The ACO metadata report (.pdf) which describe the system used to acquire and process imagery. - Polygon shapefiles (.shp) of the extent of kelp canopy at each monitoring site by year. Coordinate system used: NAD1983 UTM Zone 9N This data package is a component of the MaPP Regional Kelp Monitoring Program (RKMP) whose goals (among many) are to monitor the extent and condition of kelp forests across all four sub-regions in recognition of kelp’s ecological, cultural and economic importance. The use of this dataset requires permission from both the MaPP and the Hakai Institute. Please communicate and/or collaborate with Hakai and MaPP if you are considering using this dataset for manuscripts or other forms of reporting. Contact Luba Reshitnyk (luba@hakai.org) or data@hakai.org for more information about data access and opportunities to collaborate with the Hakai Institute. Contact Sarah Schroeder (sschroeder@mappocean.org) and Mairead Norton (maireadnorton54@gmail.com) for more information about data access to and opportunities to collaborate with MaPP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".