Bibliographic record
Abstract
Understanding the biogeography of a species begins by mapping its presence over time and space. The use of home ranges, breeding and feeding areas, migration paths and movement patterns between the two are also inherent to their ecology. However, this is an overly simplified view of life histories. It ignores nuanced and complex exchanges and responses to the environment and between conspecifics. Having previously advocated for a more species-centric approach in a discussion of ‘whale geography’, I look to better understand the driving factors of migrations, and the information streams guiding the movement, which is key to the biogeography of large whale species. First, I consider the processes underlying the navigation capacities of species to complete migration, and how, and over what scales, sensory information contributes to cognitive maps. I specifically draw on examples of large-scale, en masse migrators to then apply this to whales. I focus on the acoustic sense as the principal way whales gain and exchange information, drawing on a case study of grey whale ( Eschrichtius robustus) calling behaviour to illustrate my arguments. Their consistent employment of far-propagating calls appears to be tied to travel behaviours and probably aids navigation and social cohesion. The range over which calls are being propagated to conspecifics, or perhaps being echoed back to the individual, underlies the distance over which the cognitive maps are being both formed and employed. I believe understanding these processes edges us closer to understanding species biogeography.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".