GVI: Sample data for computing VGVI. Vancouver, BC and Manchester.
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
This is a supplement for the GVI: Greenness Visibility Index R package. Description: This dataset contains raster (TIFF) data for computing the VGVI for the City of Vancouver and Manchester. <strong>Greater Manchester:</strong> Digital Terrain Model (DTM): Spatial Resolution: 5m Source: LIDAR Composite DTM 2017 - 50cm Licence: Open Government Licence (OGL) File name: GreaterManchester_DTM_5m.tif<br> Digital Surface Model (DSM): Spatial Resolution: 5m Source: LIDAR Composite DSM 2017 - 50cm Licence: Open Government Licence (OGL) File name: GreaterManchester_DSM_5m.tif<br> Greenspace Mask: Spatial resolution: 5m Source: Dennis et al. 2017 Licence: Open Government Licence (OGL) File name: GreaterManchester_GreenSpace_5m.tif <strong>Vancouver:</strong> Digital Terrain Model (DTM): Spatial Resolution: 1m Source: Canada’s Open Government Portal Licence: Open Government Licence - Canada File name: Vancouver_DTM_1m.tif<br> Digital Surface Model (DSM): Spatial Resolution: 1m Source: Canada’s Open Government Portal Licence: Open Government Licence - Canada File name: Vancouver_DSM_1m.tif<br> Greenspace Mask: Spatial Resolution: 2m Source: Land Cover Classification 2014 - 2m LiDAR Licence: Metro Vancouver File name: Vancouver_GreenSpace_2m.tif<br> Landuse Spatial Resolution: 2m Source: Land Cover Classification 2014 - 2m LiDAR Licence: Metro Vancouver File name: Vancouver_LULC_2m.tif
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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