Image dataset of various soil types in an urban city
Bibliographic record
Abstract
LAR.i Laboratory - Université du Québec à Chicoutimi (UQAC) 2021-08-24 Name: Image dataset of various soil types in an urban city Published journal paper: Gensytskyy, O., Nandi, P., Otis, M.JD. et al. Soil friction coefficient estimation using CNN included in an assistive system for walking in urban areas. J Ambient Intell Human Comput 14, 14291–14307 (2023). https://doi.org/10.1007/s12652-023-04667-w This dataset contains images of various types of soils and was used for the project "An assistive system for walking in urban areas". The images were taken using a smartphone camera in a vertical orientation and are high-quality. The files are named with two characters, being the first letter and last letter of its class name, following by their number. Capture location : City of Saguenay, Quebec Canada. Class count : 8 Total number of images : 493 Classes and number of images per class: Asphalt (89) Concrete (80) Epoxy_coated_interior (34) Grass (90) Gravel (58) Scrattered_snow (40) Snow (68) Wood (34)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.018 |
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".