Collective decision‐making in aquatic mammals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 whales Orcinus orca and bottlenose dolphins Tursiops sp . and consensus decisions in sperm whales Physeter 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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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 it