Bibliographic record
Abstract
The purpose of this data acquisition was to collect new LiDAR data in support of the Nanwakolas 50 Watersheds Project. The acquisition plan was guided by three complimentary goals: Acquire data for 10 primary watersheds that have stream temperature data but no corresponding LiDAR data. The new LiDAR will allow the NC50 team to include these areas in stream temperature models that consider solar radiation. Acquire new data in areas where the NC50 team is using field measures of solar radiation (canopy photographs) to validate a LiDAR based model of subcanopy solar radiation. This involves one AOI on Quadra Island and one AOI in the Salmon River watershed on Vancouver Island. The new acquisition reduces the time gap between the LiDAR and field validation datasets (canopy photos). Acquire new data in a sample of the small forested catchments being used by the NC50 team to model stream temperature as a function of subcanopy solar radiation and other factors. The new acquisition reduces the time gap between the LiDAR and stream temperature datasets. The team leading the Nanwakolas 50 Watersheds Project provided the following general description of the project for context: The Nanwakolas 50 Watersheds Project was an innovative Indigenous-led science partnership to monitor and develop tools to address the threats posed by climate change and forest management on salmon habitat in the territories of the Nanwakolas member First Nations. The Nanwakolas 50 Watersheds Project was led by the Nanwakolas Council and five of its member Nations (We Wai Kai, Wei Wai Kum, Tlowitsis, Mamalilikulla and K’ómoks First Nations) in close partnership with the Hakai Institute. Funding for the Nanwakolas 50 Watersheds Project was provided by Fisheries and Oceans Canada and the Province of British Columbia through the BC Salmon Restoration and Innovation Fund. Nanwakolas Council, the Nanwakolas member First Nations, and the Hakai Institute (Tula Foundation) made significant in-kind contributions to the project.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".