Bibliographic record
Abstract
8 car sharing, 213 Autolib, 213 Avoid-Shift-Improve (ASI), 153À4 BahnCard, 25, 189 Barcelona public bicycle rental system, 6À7 Barclaycard Cycle Hire, 161 Belgium car sharing, 209À10 Bicycle sharing, 150À1, 155À6, 160À1, 163, 180, 189 bicycle rental systems, 6, 7, 160, 161 Call a Bike, 160, 161 electric bikes (e-bikes), 161, 170, 171 France, 7, 161 free-floating, 160 Germany, 160, 163, 189 integrated services see Integrated e-mobility services mobile internet and, 173À4 Multicity Carsharing, 189 Netherlands, 160 number of bicycles, 161, 162 number of programmes, 162 Seoul, 161 Shanghai, 161 United Kingdom, 161 United States, 161 ve´lo'v, 161 see also Cycling Blablacar, 160 BMW DriveNow, 157, 159, 163, 189 Built environment children's travel modes and, 45À6, 51, 61, 68À9 Bus services age of users, 95, 103 competitive tendering, 97À8, 100 gender of users, 95, 102À3 Malta see Malta modal shift, 96À7, 100 quality European Committee for Standardisation, 96, 102 Malta, 100, 105À7, 108À10, 111 service reforms, 97À8 Malta, 93À4, 100À14 see also Public transport Call a Bike, 160, 161 Canada car sharing, 159, 210 Cancer, 2 Car ownership, 150, 171 car sharing compared, 165À70 costs, 208 developing countries, 155 environmental impacts, 152À3 impact of car sharing, 158, 209À10, 211 increase in, 152, 154À5 Israel, 70 United States, 209 see also Car use Car pooling see Ride sharing Car sharing, 150À1, 154, 155, 156À9, 161, 164, 180, 189, 205À25 Australia, 213 Autolib, 213 Belgium, 209À10 business members, 208, 209, 215, 216 Canada, 159, 210 car ownership compared, 165À70 Car2Go, 153, 154, 157, 159, 163, 189, 213 characterization of systems, 212À14 City CarShare, 209 CO 2 emissions, 206, 211, 218, 219, 221 cost-effectiveness, 208À9, 222À3, 224 DriveNow, 157, 159, 163, 189 eConnect project, Osnabru¨ck, 165À70, 172 electric cars (e-cars), 154, 170À1, 213À14 see also Integrated e-mobility services energy consumption, 206, 207, 211, 216, 218, 219, 221, 223 France, 157, 158, 197, 198, 200 fuel efficiency, 211 Germany, 154, 159, 163, 209, 210, 211, 213 greenhouse gases (GHG), 211 Hertz on demand, 213 I-Go Car Share, 209 impacts, 208À12 on car ownership, 158, 209À10, 211 on kilometers traveled, 210, 211 on transport usage, 210 individual members, 208, 210, 215, 216 Ireland, 211 kilometers travelled and, 210, 211 literature, 158 MobCarsharing, Lisbon, 209, 214À25 mobile internet and, 173À4 Multicity Carsharing, 154, 157, 159, 163, 189 Netherlands, 154, 210 parking and, 211, 212, 157, 180 Portugal, 209, 214À25
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.703 | 0.726 |
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".