Vertebrate social communication: Ecological and evolutionary insights from social signals
Bibliographic record
Abstract
SignalsWilson (1975) defines communication as "the action on the part of one organism (or cell) that alters the probability pattern of behavior in another organism (or cell) in a fashion adaptive to either one or both the participants".Only a definition this broad can capture all the term "communication" encompasses; eavesdropping -where only the receiver benefits from detection of the signal or cue, manipulation -where the signaler benefits at the expense of the receiver, and what Peter Marler (1977) described as "true communication", where both the signaler and receiver derive benefit from the information conveyed between the participants.It is not an exaggeration to say that communication is the glue that binds societies together: just as cells within each living organism must be in constant communication to coordinate their activities, the individuals and their relationships that collectively define a society require ongoing, and often highly intricate forms of communication.Social signals then, are the various contact, recognition, recruitment, territorial, alarm, distress, arousal, intent, reproductive, and mobbing signals that serve to modulate social interactions among organisms.While the methods employed in studying communication have changed dramatically with advances in technology (Terhune, 2011), understanding the information content of signals remains the most valuable method of determining what is important to the organism from the organism's own perspective (Marler, 1961; but see Owren et al. 2010).As such, deciphering the information conveyed by social signals provides valuable insight into both the proximate and ultimate factors shaping organismal life-history and behavior, including both perceptual and underlying cognitive abilities (e.g.,
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".