Bibliographic record
Abstract
Ryan King and I were tasked with researching and creating maps that depicted the details of the 15 largest waterfront cities (based on population size) around the Great Lakes Basin in the US and Canada. These maps would contain information detailing the city limits, industrial zones based on municipal zoning designations, and heavy rail lines for each of the 15 cities. The maps were created in Adobe Illustrator using information retrieved from various mapping resources found online, like ArcGIS, and various zoning designation maps from municipal websites. These maps were displayed at the ‘Reimagining Water’ NSF workshop in July 2019 held at the UWM School of Frewshwater Sciences and led by Professor of Architecture James Wasley. The attendees from around the Great Lakes were encouraged to mark up these maps with resources, contact information, ongoing and proposed projects about sustainable urban waterfront systems, which we then compiled to create a “profile” for each major city. These profiles will be used as a resource for future students or professionals of differing disciplines to use for connecting between the two groups, as well as providing a base for future research paths. Their first use will be in responding to the call for the creation of an NSF Research Network on Sustainable Urban Systems that is expected in the next few months.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".