Morphological plasticity in the kelp<i>Nereocystis luetkeana</i>(Phaeophyceae) is sensitive to the magnitude, direction, and location of mechanical loading
Bibliographic record
Abstract
Nereocystis luetkeana is a canopy-forming kelp that exhibits morphological plasticity across hydrodynamic gradients, producing broad, undulate blades in slow flow and narrow, flattened blades in fast flow, enabling thalli to reduce drag while optimizing photosynthesis. While the functional significance of this phenomenon has been well studied, the developmental and physiological mechanisms that facilitate the plasticity remain poorly understood. In this study, we conducted three experiments to characterize how the (1) magnitude, (2) direction, and (3) location of plasticity-inducing mechanical stimuli affect the morphology of Nereocystis blades. We found that applying a gradient of tensile force caused blades to grow progressively longer, narrower, less ruffled, and heavier in a linear fashion, suggesting that Nereocystis is equally well adapted for all conditions within its hydrodynamic niche. We also found that applying tension transversely across blades caused the growth response to rotate 90°, indicating that there is no substantial separation between the sites of stimulus perception and response and suggesting that a long-distance signaling mechanism, such as a hormone, is unlikely to mediate this phenomenon. Meristoderm cells showed morphological changes that paralleled those of their respective blades in this experiment, implying that tissue-level morphology is influenced by cell growth. Finally, we found that plasticity was only induced when tension was applied directly to the growing tissue, reinforcing that long-distance signaling is probably not involved and possibly indicating that the mechanism on display generally requires an intercalary meristem to facilitate mechanoperception.
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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 | 0.001 |
| 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".