Biomechanical consequences of branching in flexible wave‐swept macroalgae
Bibliographic record
Abstract
Wave-swept macroalgae present an excellent system for studying the effects of chronic physical stress on the morphological evolution of plants. Wave-induced water velocities impose great drag forces, leading to a morphological tradeoff between light interception and drag reduction/tolerance. What are the hydrodynamic consequences of morphological diversification, such as increased branching? Drag was measured on artificial macroalgae of constant 'photosynthetic' area, but differing branching patterns, in a high-speed flume at water velocities up to 3.5 m s(-1). A meta-analysis was used to compare dislodgement forces of branched and unbranched species of comparable sizes in the field to determine if drag-prone morphologies had greater attachment strengths. Branched fronds experienced greater drag than unbranched fronds of the same size. Greater drag in branched forms was not the result of increased projected area but probably resulted from greater pressure or friction drag. In the field, branched species resisted greater dislodgement forces than unbranched species of comparable size, suggesting that branched species compensate for increased drag with stronger attachment to the substratum. Branching has clear biomechanical consequences, increasing drag and the need for increased attachment. This raises questions about physiological and ecological advantages that may have driven the repeated evolution of biomechanically costly, branched morphologies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".