Electrolocation without an electric image
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.
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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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".