Characterization of real objects by an active electrolocation sensor
Bibliographic record
Abstract
Weakly electric fish use a process called 'active electrolocation' to orientate in their environment and to localize objects based on their electrical properties. To do so, the fish discharge an electric organ which emits brief electrical current pulses (electric organ discharge, EOD) and in return sense the generated electric field which builds up surrounding the animal. Caused by the electrical properties of nearby objects, fish measure characteristic signal modulations with an array of electroreceptors in their skin. The fish are able to gain important information about the geometrical properties of an object as well as its complex impedance and its distance. Thus, active electrolocation is an interesting feature to be used in biomimetic approaches. We used this sensory principle to identify different insertions in the walls of Plexiglas tubes. The insertions tested were composed of aluminum, brass and graphite in sizes between 3 and 20 mm. A carrier signal was emitted and perceived with the poles of a commercial catheter for medical diagnostics. Measurements were performed with the poles separated by 6.3 to 55.3 mm. Depending on the length of the insertion in relation to the sender-receiver distance, we observed up to three peaks in the measured electric images. The first peak was affected by the material of the insertion, while the distance between the second and third peak strongly correlated with the length of the insertion. In a second experiment we tested whether various materials could be detected by using signals of different frequency compositions. Based on their electric images we were able to discriminate between objects having different resistive properties, but not between objects of complex impedances.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".