Macroalgal features and their influence on associated biodiversity: implications for conservation and restoration
Bibliographic record
Abstract
Studies examining the relationship between seaweeds and the diversity of associated organisms have been attempted far less than those focusing on the influence of terrestrial plants. That is troublesome considering the growing number of studies reporting the decline or local loss of macroalgae because of ocean warming and climate change. Since the fate of macroalgae will have an influence on associated organisms, this brief overview examined the different roles played by macroalgae, making the distinction between morphological features associated with individual seaweed species and those associated with populations or habitats. Most studies at both (individual and population) levels have identified positive relationships between morphological features such as structural complexity (including fractal dimensions) and invertebrate biodiversity, and the abundance of various faunistic groups. Some of these relationships are stronger than others, often with complex outcomes, suggesting that the current and future ecological benefits provided by macroalgae are strongly species- and habitat-dependent. While the displacement or local-scale loss of seaweeds may continue because of climate change, the features identified here may become useful in light of conservation and restoration 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".