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
We present a procedural technique for the controllable synthesis of detailed terrains. We generate terrains based on a sparse curve network representation, where interconnected curves are distributed in the plane and can be procedurally assigned height. We employ path planning to procedurally generate irregular curves around user-designated peaks. Optionally, the user can specify base signals for the curves. Then we assign height to the curves using random walks with controlled probability distributions, a process which can produce signals with a variety of shapes. The curve network partitions space into individual patches. We interpolate patch heights using mean value coordinates, after which we have a complete terrain heightfield. Our algorithm enables users to obtain prominent features with lightweight interaction. Increasing the density of curves and roughness of curve profiles adds detail to the synthetic terrains. The curves in a network are organized into a hierarchy, where the major curves are created first and the curves constructed at later stages are affected by earlier curves. Our approach is capable of producing a variety of landscapes with prominent ridges and distinct shapes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| 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