Cognition-Based Extraction and Modelling of Topographic Eminences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Terrain is generally stored in GIS as an elevation field, whereas human cognition of the landscape is usually object based. To address this mismatch of terrain data models, we propose object-based terrain representation, using topographic eminences, which are landforms that rise up conspicuously from the ground to visibly dominate the landscape, to illustrate our case. We propose a cognition-based methodology for automated detection and delineation of eminences from digital elevation models (DEMs). Alternative conceptualizations of the landscape can be realized by simple manipulation of intuitive parameters such as a peak's relative height and distance. Our approach delimits the extent of eminences based purely on topographic gradient and aspect, much like the delineation of ridges as watershed boundaries. Smaller eminences can be incrementally aggregated into larger cognitive wholes, enabling scale-sensitive landscape reconstruction. The ability to integrate field and object views of the landscape is essential for raster–vector data-layer integration; therefore, we also discuss some database-modelling and ontology-development strategies to manage the extracted landforms within a GIS.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it