Summary of baseline kelp forest surveys within and adjacent to Gwaii Haanas National Park Reserve, National Marine Conservation Area Reserve and Haida Heritage Site, Haida Gwaii, British Columbia, Canada
Bibliographic record
Abstract
In anticipation of the establishment of the Gwaii Haanas National Marine Conservation Area Reserve on Haida Gwaii, British Columbia, Simon Fraser University, Parks Canada, Haida Fisheries Program and the Department of Fisheries and Oceans Canada initiated a collaborative kelp forest ecosystem monitoring program to establish baselines against which future changes could be evaluated. Underwater visual surveys of subtidal reef-associated fish, invertebrate and macroalgal communities were commenced during the summer of 2009 in the shallow rocky reef ecosystems of Haida Gwaii. Nine monitoring sites were initially established along the east coast of Louise Island, Lyell Island and Kunghit Island. Three additional sites were established on the west coast of Kunghit Island in 2010. Annual surveys were attempted for five years (2009-2013) at all sites. Surveys included replicate shallow (5-8 m) and deep (10-13 m) horizontal belt transects run parallel to shore for reef-associated fish (30x4 m) and conspicuous benthic macroinvertebrates (30x2 m), and quadrats (1x1 m) for urchins and macroalgae. From 2010 onward, the species composition of the kelp bed from shore to outer edge was examined with a vertical (perpendicular to shore) belt transect to survey kelp stipe density. Finally, all kelp was collected from within a 1m2 quadrat placed haphazardly in the middle of the bed; all kelp within this quadrat was sorted by species and weighed. These data provide a useful baseline against which to evaluate the ecological effects of 1) current spatially explicit management policies such as rockfish conservation areas (RCAs), 2) future marine zoning policies expected to be implemented in Gwaii Haanas, and 3) anthropogenic climate change and natural oceanographic forcing functions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".