Electrolocation without an electric image
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
Weakly electric fish sense their environment in the dark using a self-generated electric field. Perturbations in the field caused by different objects are encoded by an array of sensors on their skin. The information content in these perturbations is not entirely clear. Previous work has focused on the so-called electric image (or field perturbation), which is the difference in the field at the skin surface, with and without the object present. Various features of the electric image have been shown to provide information about an object, including location. However, electric image based algorithms require information about the electric field under two qualitatively distinct conditions, and in many situations, prior information about the unperturbed field is not available. Here, we consider the more general problem of object localization with electric sensing when only instantaneous measures of the electric field are available. We show that this problem is solvable when field measurements for two slightly different object locations are considered (such as those occurring during relative motion). In doing so, we provide a direct link between sensory flow (i.e. the moment-to-moment fluctuations in raw sensory input) and electrosensory-based object localization.
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 | 0.001 |
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 it