Bibliographic record
Abstract
The primary objective of the Geoconnections project is to develop and test a scalable Light Detection and Ranging (LiDAR) cloud optimized point cloud (COPC) and cloud-optimized GeoTIFF (COGs) Application Programming Interface (API) that is web-based and easily accessible by multiple user groups. The API will be designed to make LiDAR data discoverable and also provide a set of simple analysis tools and export format types (e.g.,GeoTIFF & geopackage) to aid in landscape change detection. The API will be built on an interoperable cloud-based system that will allow input of high-density LiDAR data into existing pre-processed data staging platforms and connection with an accessible online application. The project will use time series COPC LiDAR data of three case study regions in British Columbia that have experienced landscape altering events due to climate change. 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.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.091 | 0.046 |
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".