Benthic Diversity of Sessile Organisms in Rocky Reef Habitats of Coastal Brazil: An Insight into the Implementation of Marine Protected Areas
Bibliographic record
Abstract
Marine Protected Areas are increasingly becoming a tool of choice for conservation and management of marine resources and ecosystems. Data on biodiversity are necessary to assist in establishing protected areas for conservation objectives to be met. Toward that effect, we investigated reef biodiversity patterns in three large-scale coastal regions of Brazil. The study areas comprised of an upwelling region, an adjacent high impacted region, and a more distant marine park. We surveyed four reef sites in each study region. Species counts of sessile benthic organisms, substrate relief, and average monthly water temperatures were recorded during the surveys. Benthic organisms were identified to the lowest taxa possible using still photos. Biodiversity was estimated using Shannon’s index on richness of organism taxa. Diversity was highest at the upwelling and high-impacted areas. No substrate relief patterns were found. Temperature readings showed lower average values at the upwelling and high impacted regions. Our results favor the upwelling region for establishment of a Marine Protected Area. Moreover, the similar diversity between the upwelling and the high impacted regions showed evidence of spillover effects from the former into the latter region, further demonstrating the importance of the former region for conservation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".