Improving accessibility for pederstrians with geographic information
Bibliographic record
Abstract
Osajulkaisut: Publication 1: Mari Laakso, Tapani Sarjakoski, and L. Tiina Sarjakoski. 2011. Improving accessibility information in pedestrian maps and databases. Cartographica, volume 46, number 2, pages 101-108. University of Toronto. DOI:10.3138/carto.46.2.101 Publication 2: L. Tiina Sarjakoski, Pyry Kettunen, Hanna-Marika Flink, Mari Laakso, Mikko Rönneberg, and Tapani Sarjakoski. 2012. Analysis of verbal route descriptions and landmarks for hiking. Personal and Ubiquitous Computing, volume 16, number 8, pages 1001-1011. DOI:10.1007/s00779-011-0460-7 Publication 3: Mari Laakso, Tapani Sarjakoski, Lassi Lehto, and L. Tiina Sarjakoski. 2013. An information model for pedestrian routing and navigation databases supporting universal accessibility. Cartographica, volume 48, number 2, pages 89-99. University of Toronto. DOI:10.3138/carto.48.2.1837 Publication 4: Mari Laakso and L. Tiina Sarjakoski. 2010. Sonic maps for hiking—Use of sound in enhancing the map use experience. The Cartographic Journal, volume 47, number 4, pages 300-307. DOI:10.1179/000870410X12911298276237 Publication 5: Mari Laakso, Hanna-Marika Halkosaari, Tapani Sarjakoski, and L. Tiina Sarjakoski. 2013. User experiences with voice-based descriptive map content in a hiking context. In: Thomas Jekel, Adrijana Car, Josef Strobl, and Gerald Griesebner (editors). Creating the GISociety. Proceedings of the GI_Forum 2013 Conference. Salzburg, Austria. 2-5 July 2013. Berlin / Offenbach, Germany. Herbert Wichmann Verlag, VDE Verlag. Pages 49-58. ISBN 978-3-87907-532-4. DOI:10.1553/giscience2013s49
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.163 | 0.065 |
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".