Scented Cybercartography: Exploring Possibilities
Bibliographic record
Abstract
Olfactory cartography is part of the emerging discipline of cybercartography (Taylor 2003), a transdisciplinary endeavour that investigates, among other things, the integration of multimedia, multi-sensory, and multimodal data into digital atlases and maps. The physiology and psychology of the olfactory system, its special characteristics, its influence on performance and memory, and some of the issues that make the study of olfaction difficult are addressed. Characterizing, classifying, and labelling scents is problematic, and it is recommended that methods from other communities of practice be adopted and adapted by cartographers. Literature from a wide range of disciplines, including olfactory geography, is reviewed, and a number of innovative ideas are provided. In addition, olfactory applications in different areas such as marketing, art installations, film, and virtual environments are described, as are a range of currently available olfactory diffusion devices. These, however, have not been explored in a cartographic context, nor have they undergone usability testing. We conclude that it is too early to provide cartographic guidelines and methods but that scented applications, odour diffusion technologies, and olfactory data collection methods provide knowledge that can be applied toward developing a scented cartography.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".