Measuring boldness in decapod crustaceans: an overview of methodological approaches and potential caveats
Bibliographic record
Abstract
Behaviours such as boldness (the willingness to take risks) vary within and among species and can influence fitness by indirectly affecting resource competition, mortality risk, reproductive success, and dispersal. As such, many studies have investigated boldness in decapod crustaceans, a group of considerable ecological and economic importance. An initial review of these studies suggested outcome inconsistencies that warrant an examination of the approaches used to measure boldness. Boldness is often quantified by measuring behaviours such as latency to emerge from a shelter, exploration of novel environments, or activity following a threat. Hence, we provide an overview of the growth of research and taxonomic representation and analyse the gaps in the methodological approaches for studies examining boldness in decapods over 20 years (2004 – 2024). An examination of 78 studies indicates steady growth that has been narrow in terms of subject taxonomy and methodologies to measure boldness. The outcomes of these studies are often affected by design choices such as the behaviours measured (some widely used, like shelter use, others more controversial, such as exploratory behaviours), the sex, age, condition, and origin of the subjects, and the experimental or rearing conditions (e.g., acclimation times, density, feeding regime, and temperature). Understanding how methodological choices influence decapod boldness is necessary to improve temporal consistency, ensure reproducibility and reliable comparisons among studies, thereby facilitating meta-analyses. Otherwise, inconsistent reporting of design choices may limit the accuracy and feasibility of such meta-analyses, hindering the synthesis of results.
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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 | 0.212 | 0.326 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".