Future Research Directions on the “Elusive” White Shark
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
White sharks, Carcharodon carcharias, are often described as elusive, with little information available due to the logistical difficulties of studying large marine predators that make long-distance migrations across ocean basins. Increased understanding of aggregation patterns, combined with recent advances in technology have, however, facilitated a new breadth of studies revealing fresh insights into the biology and ecology of white sharks. Although we may no longer be able to refer to the white shark as a little-known, elusive species, there remain numerous key questions that warrant investigation and research focus. Although white sharks have separate populations, they seemingly share similar biological and ecological traits across their global distribution. Yet, white shark’s behaviour and migratory patterns can widely differ, which makes formalising similarities across its distribution challenging. Prioritisation of research questions is important to maximise limited resources because white sharks are naturally low in abundance and play important regulatory roles in the ecosystem. Here, we consulted 43 white shark experts to identify these issues. The questions listed and developed here provide a global road map for future research on white sharks to advance progress towards key goals that are informed by the needs of the research community and resource managers.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it