Bibliographic record
Abstract
The number of reports highlighting the services provided by seagrass beds continues to be matched by those reporting on local seagrass declines across the world coastlines. Among the many factors driving the fate of seagrass beds, this Mini Review focuses on the interactions between seagrasses and algae, more specifically, macroalgae. Seagrasses are known to respond to sudden increases in co-occurring macroalgae, and the ongoing warming of ocean waters suggests that these interactions are most likely to grow in frequency and possibly in intensity in the decades ahead. What remains unclear is the nature (positive, neutral, negative) and the local outcome of those interactions. We examined the published evidence on explicit seagrass-algal interactions and found that in most cases these interactions have been negative, with seagrass species most often found at a competitive disadvantage with regards to macroalgae. Rising ocean temperatures are likely to add to this imbalance as at least some studies already suggest that the negative effects of macroalgae and warming are either additive or synergistic. The further examination of these effects will help predict likely future scenarios and aid in the prioritization of conservations efforts.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".