Less Than the Sum of Its Parts: Blade Clustering Reduces Drag in the Bull Kelp,<i>Nereocystis luetkeana</i>(Phaeophyceae)
Bibliographic record
Abstract
Nereocystis luetkeanais a large, canopy‐forming kelp that is commonly found in nearshore waters between Alaska and California. Despite regularly reaching lengths in excess of 30 m, this alga demonstrates a remarkable ability to endure hydrodynamically stressful environments without being dislodged by waves or currents. While morphological aspects of this kelp, including its long flexible stipe, have been shown to reduce drag, blade clustering has never been directly tested. In this study, we examined how the distinctive multi‐bladed morphology ofNereocystisthalli limits drag in flow. We measured drag on whole kelps in a large recirculating flume and quantified how drag was affected by serial removal of blades. We then compared measured drag with predictions of “additive drag”, which we defined as the sum of the drag that each blade experiences in isolation. We found that, on average, intact thalli experience only 37% of the predicted additive drag when subjected to a flow rate of 0.40–0.50 m · s−1. Our results indicate that the subdivision of the photosynthetic area into multiple blades has a drag‐reducing effect onNereocystisthalli. We found drag increases less than proportionally with the cumulative area, meaning the contribution of individual blades to overall drag decreases with increasing blade number. That is, as thalli develop, each additional blade incurs a reduced hydrodynamic cost, perhaps lending insight into how thalli can grow so large.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".