Biodiversity Surveys of the Gwaxdlala/Nalaxdlala Indigenous Protected and Conserved Area (IPCA) in Knight Inlet, British Columbia
Bibliographic record
Abstract
This data package contains the datasets and reports pertaining to biodiversity surveys in Knight Inlet, British Columbia, Canada. This work is an on-going, collaborative project between the Hakai Institute, Nanwakolas Council, and Mamalilikulla Nation. In September 2020 the Hakai Institute conducted an initial round of biodiversity surveys of the Gwaxdlala/Nalaxdlala (Lull Bay/Hoeya Sound) Indigenous Protected and Conserved Area (IPCA) in Knight Inlet, British Columbia, Canada using scuba surveys and environmental DNA (eDNA) sampling. Visual dive surveys and eDNA samples were paired at six sites in Knight Inlet, while dive surveys were conducted at three additional sites. eDNA samples were collected from the surface, midwater column, and bottom (8-25m depth) at each site, while dive surveys focused on the bottom (benthic) habitat. Dive and eDNA survey sites were selected based on the interests of the Mamalilikulla Nation and Nanwakolas Council in assessing the area for consideration as a marine protected area site in a broader network. This data package is a component of the Hakai Institute’s Nearshore program. The Hakai Institute’s Nearshore research and monitoring program investigates the role of marine habitats and their associated communities in the face of change. The environmental DNA sampling and genomic data processing component of the data package is part of the Hakai Institute Biomolecular Observing Network (HI-BON), an endorsed project by the UN Decade of Ocean Science for Sustainable Development. Permission to use this dataset must be granted by the Hakai Institute and Mamalilikulla Nation (please contact angeleen.olson@hakai.org for further dialogue) to the individual who makes the data request. Data can only be used for the permitted purpose and are not to be shared or repurposed. Please refer to the README.txt file included in this data package for more information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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; 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".