Benthic algae and diatom communities in seagrass meadows under three different human impact regimes in Bocas del Toro, Panamá
Bibliographic record
Abstract
In Bocas del Toro, Panamá, widespread tourism has been the main source of revenue and has become an increasing threat to seagrass meadows and the organisms they support. This study aimed to investigate and describe algae and diatom communities under three different regimes of anthropogenic disturbance: high, medium and low human impact. The biodiversity was analyzed by measuring the algae and diatom assemblages with Shannon-Weiner’s Biodiversity Index, Evenness Index and Sorensen’s coefficient. The data obtained from these three diversity indices were compared to the areas along an anthropogenic disturbance gradient of high, medium and low impact. A total of 12 species of algae and 25 genera of diatoms were found using the marine belt transect and quadrat method in triplicate for each site. Analysis showed that in the site with high human impact, the seagrass density was significantly lower, while algae biodiversity and abundance, and diatom biodiversity, were significantly higher. This study demonstrated that algae and diatom communities do, in fact, change in differing human impact sites. Thus, algae and diatoms can be accurate bioindicators of water quality and can be used to limit human impact on seagrass meadows.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".