Bibliographic record
Abstract
Research has shown that a variety of organisms encode the geometry of their environment to re-establish orientation (i.e., reorientation). This has been shown in species ranging from rats to bees, and has been shown to be an automatic process. This automatic process of encoding geometry has been taken as evidence for a geometric module in the brain of these species. However, it is currently not known whether reptiles also use geometry to reorient. This study will investigate the use of geometric cues for reorientation in a corn snake. The snake will be trained to locate a goal in a corner of a rectangular arena. At each corner, a unique landmark will be available. Once the snake has learned to locate the target corner, it will attempt to relocate the corner in the absence of the landmarks. If the snake has encoded the geometry of the arena during training, it should be able to locate the goal, and will make rotational errors (i.e., mistaking the diagonally opposite corner for the correct corner). This rotational error would provide evidence that the snake has encoded the geometry even though it was trained to rely on the landmarks during training. This would provide support for the existence of a geometric module in the snake’s brain, and potentially in the general reptilian brain as well Discipline: Biological Sciences Faculty Mentor: Dr. Jean-Francois Nankoo
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".