Automating geoprocessing tasks to create city wide layers
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
Annually, Spatial and Numeric Data Services (SANDS) receives 434 Digital Aerial Survey (DAS) datasets, in AutoCAD format, from the City of Calgary. Each dataset covers an Alberta Township System (ATS) section (Figure 1) and is made up of five layers containing “surface features and topography” information “derived from 1:5000 aerial photos”1. University of Calgary students regularly utilize these files in their research, however, in some cases they are looking for specific features covering an area much larger than an ATS section and in a more geographic information system (GIS) friendly format. To derive city wide products, from the DAS files, required reiterating through numerous geoprocessing operations. These repetitive and time consuming tasks were automated and accomplished within a day, using Python and ArcPy, instead of weeks if executed manually. The next section details the steps that were taken to create the outputs.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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