Spatial extent of surface canopy kelp derived from fixed-wing surveys (2024), North Coast, 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 several areas on the North Coast 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 July 6th and 7th, 2024. The survey regions included (1) Ryan Point to Duncan Bay, (2) Stephens and Prescott Island, (3) north west Porcher Island and (4) south west Porcher Island. 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 Aerial Coastal Observatory (a fixed-wing aircraft). Imagery collection and orthomosaic processing is described in associated reports linked in this record. Kelp delineation: Surface kelp canopy is represented by polygons which were digitized using the Hakai Institute Kelp-O-Matic AI tool. Datasets were reviewed by a 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. This data was collected for the purpose of deriving datasets on the distribution of kelp. Data collected as part of the Hakai-MaPP kelp mapping partnership will be co-managed by both groups. Permission to use these data is required by Hakai and the MaPP North Coast member First Nations (Gitga'at, Gitxaała, Haisla, Kitselas, Kitsumkalum, and Metlakatla). 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 North Coast (northcoast_metadata@mappocean.org) for more information about data access to and opportunities to collaborate with MaPP.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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; both teacher heads agree on what is shown here.
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".