All Dogs Go to Prince George’s County: Finding a Home for a Second Animal Services Facility
Bibliographic record
Abstract
As a continuation of the Fall 2017 PALS project, Spring 2018 semester students from the community planning and engineering programs used advanced computer mapping tools (geographic information system, or GIS) to provide Prince George’s County with potential sites to build a second animal shelter. The team attempted to find land that the county already owned, but none of the parcels met the requirements. The team found a solution to this problem by including distressed shopping centers in the site analysis. From these forty shopping centers, eleven were chosen for their location within the county’s Growth Policy Center. We used ArcGIS Online to understand how many potential adopters could reach these facilities within fifteen and thirty minutes. We then chose the five shopping centers closest to the most people and households. We present these to Prince George’s County as potential candidate sites. The link for the county to access the ArcGIS Online website to view the maps and site locations is: http://uofmd.maps.arcgis.com/home/item.html?id=ea8cc7f3ca154064939db517e24b4606.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".