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
Navigation is coordinated and goal-directed movement through the environment by organisms or intelligent machines. It involves both planning and execution of movements. It may be understood to include the two components of locomotion and wayfinding. Locomotion is body movement coordinated to the local surrounds; wayfinding is planning and decision making coordinated to the distal as well as local surrounds. Several sensory modalities provide information for navigating, and a variety of cognitive systems are involved in processing information from the senses and from memory. Animals update their orientation – their knowledge of location and heading – as they move about. Combinations of landmark-based and dead-reckoning processes are used to update. Humans also use symbolic representations to maintain orientation, including language and cartographic maps. Factors have been identified that make orientation easier in some environments than others. Neuroscience has pointed to the role of certain brain structures in the maintenance of orientation and has uncovered evidence for neurons that fire preferentially in response to an animal's location or heading. Artificial intelligence researchers develop computer models that test theories of navigational cognition or just create competent robots.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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