Bibliographic record
Abstract
Snow. It's a short word with tall urban consequences. I just experienced my first Montreal winter. It was captivating, beautiful and enduring. Now I understand why foreigners measure their residency by winters, not years. Under its 2m annual average snow fall, the French-Canadian metropolis looks and feels completely different from without. Changes in daily lifestyle are extensive. As a Melburnian, bike rider and park frequenter, I was particularly stunned by the relationship shift between user and public space. This is learnt the hard way: During mid-November, following a naive declaration for winter cycling and while dreamily admiring sprinkling snowflakes, I slid and stacked my bike on the then 15mm ground cover. I went from regular rides to five months of hibernation triggering a (knee-) deep snow obsession. The result is this snapshot of my observations of how one's interaction with public space changes, reflecting on how city operations accommodate this. The all-determining snow removal process is first explored, then how people can or can't move, and lastly use of open spaces.
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) | 0.000 | 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 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".