Place Glacier Aerial Photo and LiDAR Survey
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
Place Glacier is one of three benchmark glaciers in western Canada that scientists routinely monitor to inventory how climate change is affecting one of Canada’s important freshwater resources. Since 2015, Hakai have done over 50 aerial surveys of Place Glacier and produced a LiDAR dataset that is unparalleled in its temporal coverage for an alpine glacier. This glacier also lies within the traditional territory of the Lil’wat First Nation and Hakai Affiliate Brian Menounos has established a partnership with the Lil’wat to better understand how glaciers are projected to change in the Lil’wat territory in the decades ahead. The climate of BC’s South and Central Coast makes it particularly sensitive to climate change, with comparatively warmer winters than continental environments. The BC coast’s extreme elevation gradients, however, may provide some resilience in certain watersheds with high elevations and extensive glacier coverage. Better characterization of snow and glacier coverage will improve our ability to observe long-term change, develop and improve existing hydrological models, and provide guidance to local communities who will need to adapt. The project will be designed to meet and leverage the Natural Resource Canada (NRCan) Centre of Mapping and Earth Observation (CCMEO) Findable, Accessible, Interoperable and Reusable (FAIR+) principles by democratizing existing LiDAR data and integrating it into an open-source and cloud-based data processing workflow that can be automated and accessed by non-specialist user groups, policy makers and geospatial specialists. The deliverables of the project are envisioned to actively contribute to spatial data standards and practices that could be adopted across Canada. The project outcomes will also support evaluation on how LiDAR from the Government of British Columbia can be made more readily available to users. All LiDAR and imagery data used and made available through the application has been collected and processed by the Geospatial Team at the Hakai Institute through the Airborne Coastal Observatory (ACO) program. For more information on post processing, data quality assurance, software used, and summary of results please contact data@hakai.org
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.010 |
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