Proximate and ultimate causes of signal diversity in the electric fish<i>Gymnotus</i>
Bibliographic record
Abstract
A complete understanding of animal signal evolution necessitates analyses of both the proximate (e.g. anatomical and physiological) mechanisms of signal generation and reception, and the ultimate (i.e. evolutionary) mechanisms underlying adaptation and diversification. Here we summarize the results of a synthetic study of electric diversity in the species-rich neotropical electric fish genus Gymnotus. Our study integrates two research directions. The first examines the proximate causes of diversity in the electric organ discharge (EOD) - which is the carrier of both the communication and electrolocation signal of electric fishes - via descriptions of the intrinsic properties of electrocytes, electrocyte innervation, electric organ anatomy and the neural coordination of the discharge (among other parameters). The second seeks to understand the ultimate causes of signal diversity - via a continent-wide survey of species diversity, species-level phylogenetic reconstructions and field-recorded head-to-tail EOD (ht-EOD) waveforms (a common procedure for characterizing the communication component of electric fish EODs). At the proximate level, a comparative morpho-functional survey of electric organ anatomy and the electromotive force pattern of the EOD for 11 species (representing most major clades) revealed four distinct groups of species, each corresponding to a discrete area of the phylogeny of the genus and to a distinct type of ht-EOD waveform. At the ultimate level, our analyses (which emphasize the ht-EOD) allowed us to conclude that selective forces from the abiotic environment have had minimal impact on the communication component of the EOD. In contrast, selective forces of a biotic nature - imposed by electroreceptive predators, reproductive interference from heterospecific congeners, and sexual selection - may be important sources of diversifying selection on Gymnotus signals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".