Collective decision‐making in aquatic mammals
Bibliographic record
Abstract
ABSTRACT Collective decision‐making is an essential part of day‐to‐day life for group‐living animals. These decisions can be unshared (e.g. leadership) or shared (e.g. consensus). Aquatic mammals face particular challenges when making collective decisions, including a three‐dimensional habitat that can make group coordination and collective navigation a challenge. We systematically reviewed literature on decision‐making in non‐human mammals by examining the types of collective decisions observed and hypotheses used to structure analyses. Most of the current literature was centred around terrestrial species, particularly within primates and artiodactyls. There are no collective decision‐making studies on aquatic mammal species outside of cetaceans. Both unshared and shared decision‐making have been reported in whales and dolphins, with leadership found in killer whalesOrcinus orcaand bottlenose dolphinsTursiopssp.and consensus decisions in sperm whalesPhyseter macrocephalus. Five recommendations for decision‐making research include: 1) clearly delineating the temporal components of decision‐making, 2) standardising research to allow for comparisons, 3) considering both shared and unshared decision‐making, 4) analysing decision‐making across behavioural contexts, and 5) avoiding anthropomorphic terminology. Future studies of collective decision‐making will help us better understand how non‐human mammals overcome environmental and contextual challenges – particularly in the case of aquatic species such as cetaceans, which face challenges related to their aquatic environment and exhibit phenomena such as mass strandings.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".