Drag reduction in wave‐swept macroalgae: Alternative strategies and new predictions
Bibliographic record
Abstract
PREMISE OF THE STUDY: Intertidal macroalgae must resist extreme hydrodynamic forces imposed by crashing waves. How does frond flexibility mitigate drag, and how does flexibility affect predictions of drag and dislodgement in the field? METHODS: We characterized flexible reconfiguration of six seaweed species in a recirculating water flume, documenting both shape change and area reduction as fronds reorient. We then used a high-speed gravity-accelerated water flume to test our ability to predict drag under waves based on extrapolations of drag recorded at slower speeds. We compared dislodgement forces to drag forces predicted from slow- and high-speed data to generate new predictions of survivorship and maximum sustainable frond size along wave-swept shores. KEY RESULTS: Bladed algae were generally "shape changers", limiting drag by reducing drag coefficients, whereas the branched alga Calliarthron was an "area reducer", limiting drag by reducing projected area in flow. Drag predictions often underestimated actual drag measurements at high speeds, suggesting that slow-speed data may not reflect the performance of flexible seaweeds under breaking waves. Several seaweeds were predicted to dislodge at similar combinations of velocity and frond size, suggesting common scaling factors of dislodgement strength and drag. CONCLUSIONS: Changing shape and reducing projected area in flow are two distinct strategies employed by flexible seaweeds to resist drag. Flexible reconfiguration contributes to the uncertainty of drag extrapolation, and researchers should use caution when predicting drag and dislodgement of seaweeds in the field.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".