Gitga'at Territory Coastal Monitoring and Mapping - Airborne Coastal Observatory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Gitga’at Oceans and Lands Department has developed an integrated program that combines coastal sensitivity mapping with wave impact assessments. The goal of this initiative is to inventory critical coastal resources and evaluate the potential effects of ship wakes on vulnerable habitats. The Hakai Institute has been invited to support the Gitga’at by contributing to mapping efforts and providing guidance for long-term monitoring. Research activities focus on archaeological sites, subtidal zones, and shoreline ecosystems. The Airborne Coastal Observatory (ACO) is a collaborative program led by the Hakai Institute along with partners the University of Northern British Columbia. The ACO program offers rapid and accurate aerial observations of both terrestrial and marine ecosystems, from Icefields to Oceans, and applied across multiple scientific disciplines. Data is collected by a Piper Navajo aircraft equipped with an array of integrated Earth imaging systems and technology, including: 1) A Riegl VQ-780 airborne laser scanner; 2. Two PhaseOne iXU-RS 1000 digital medium format cameras; 3. Specim AisaFENIX Imaging Spectrometer; 4. Applanix Inertial Navigation System. All data is processed and maintained by the Hakai Geospatial Technology team. The aircraft is provided and maintained by Kisik Aerial Surveys Inc. (Delta, BC). The study area encompasses waterways within the Gitga’at Territory Marine Use Plan on the north coast, British Columbia, Canada. Four locations have been selected for ACO mapping in 2025: Kitkiata Inlet, Kishkosh Inlet, Union Pass, and the Estevan Islands.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it