Bibliographic record
Abstract
Have you ever sat in the window seat of an airplane and marveled at the miniature city below? It's easy to identify the industrial and big box retail areas with large windowless buildings and toy-looking vehicles scattered around huge surface parking lots. It is also easy to identify the residential neighborhoods with block after block of single-family homes in varying sizes organized neatly in rows. Two of the main systems consistent throughout the city are major roads and large green spaces. One is generally identified by straight lines and stoplights and the other by meandering tree cover and the occasional baseball diamond or high school track. It is more difficult to ascertain the in-between areas. Was the design intentional or organic? Where are all the “tiny” cars going? Where do people work? Can they walk to school? Where do they go for entertainment? Do they have a long commute, or do they live near their work? Can they find goods and services within walking distance of their residence? These questions all help uncover if someone lives or works in a single land use area or an economically diverse neighborhood.
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 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) | 0.006 | 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 teacher head, 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".