Increasing the dimensionality of a Geographic Information System (GIS) Using Auditory Display
Bibliographic record
Abstract
This paper describes a way to incorporate sound into a raster based classified image. Methods for determining the sound location, amplitude, type and how to create a layer to store the information are described. Hurdles are discussed and suggestions of how to overcome them are presented. As humans we rely on our senses to help us navigate the world. Sight, sound, touch, taste, and smell; they all help us perceive our environment. Although we sometimes take vision for granted, all our other senses play as important of a role in our daily lives. Even with all these senses at our disposal, the conventional GIS very uncommonly do much more than convey their information visually. We demonstrate an auditory display with a sample implementation using a classified raster image, commonly used in a GIS analysis. This was achieved using a spatial sonification algorithm initially created in a Java environment. The ultimate aim of this work is to develop an interactive mapping technology that fully incorporates auditory display, over a variety of platforms and applications. Such a tool would have the potential be of great benefit for displaying multivariate information in complex information displays.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".