Connectivity, Recruitment Variation, and the Structure of Reef Fish Communities
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
Coral reefs contain the most speciose communities of fishes on this planet, so it is appropriate to use these to explore how fish species are organized into communities. While descriptive data suggest that the diverse communities of fish on coral reefs are equilibrial assemblages of species, all finely adapted to specific and unique ecological roles, these are highly dynamic, non-equilibrial assemblages with structure driven more by patterns of recruitment and loss of individual fishes, than by patterns of resource allocation among differently adapted phenotypes. As a consequence, local assemblages differ in structure, and structure wanders through time. Individual fish are confronted by different mixes of species in different times and places. The recruitment process that drives these dynamics is complex, being governed by several mechanisms, and local populations receive some portion of their recruitment from distant sources. Information on this connectivity among local populations is critically important for management which is based increasingly on use of marine protected areas (no-take zones) both to conserve, and to provide sustainable fisheries. At present, however, we do not know the spatial scale or the extent of this connectivity, and this critical knowledge gap impedes both management, and fundamental understanding.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it