Development and use of a GIS package for broad-based needs at the Quesnel River Research Centre (central British Columbia)
Bibliographic record
Abstract
The Quesnel River Research Center (QRRC) is a base station for research in the Quesnel River watershed. Since UNBC began research at this venue there has been a need for a Geographic Information System (GIS). In the spring of 2007 a grant to the Landscape Ecology Research Program allowed this project to begin. Spatial data from various sources, such as: the Government of British Columbia, Government of Canada, and the University of Northern British Columbia was used. The QRRC GIS contains a wide range of data useful for both researchers and community members, such as: BC TRIM 2 data, Government of Canada soils maps, stream order maps, drainage basin data, and Landsat 7 images. Stream order maps and the drainage basin data were generated using Esri's Arcmap Hydro tools. This posed a challenge, as there was little documentation relating to hydrological tools for the building of these layers. This talk will present an overview of how this system was developed and how it is designed to facilitate a range of users. As well this talk will introduce some of the techniques used to overcome challenges in its development.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.017 |
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".