Namu British Columbia - 2021 - Hakai Institute - Airborne Coastal Observatory
Bibliographic record
Abstract
Hakai has committed to providing geospatial data about the Namu site to Core6 Environmental to help estimate costs in cleaning up the site. Core6 is working with the Heiltsuk FN to determine what’s involved in cleaning up the old cannery and surrounding areas. Hakai provided drone and multibeam survey data in addition to the ACO data detailed in this report. Namu – imagery collected July 18th, 2021 The Airborne Coastal Observatory (ACO) is a collaborative program led by the Hakai Institute with partners the University of Northern British Columbia and Kisik Aerial Surveying. The program offers rapid and accurate aerial observations of coastal ecosystems, from Icefields to Oceans. A Kisik Aerial Piper Navajo aircraft is packed with an array of integrated Earth imaging sensors and technology to provide highly visual and highly accurate data. Hakai’s Airborne Coastal Observatory was developed to map and monitor icefields to oceans by using a combination of airborne Lidar (Light Detection and Ranging), high-resolution imagery, and hyperspectral imagery. Combined, the ACO sensors provide data to quantify changes in seasonal snow cover and glacier mass loss. The ACO is an aerial remote sensing platform used by the Hakai Institute to survey landscapes in detail. A Piper Navajo aircraft carries an array of integrated airborne mapping sensors installed to collect data in concert. The aircraft is operated and maintained by Kisik Aerial Surveys (Delta, BC).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.092 | 0.024 |
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".