Cell wall chemistry and tissue structure underlie shifts in material properties of a perennial kelp
Bibliographic record
Abstract
Laminaria is an abundant kelp genus in temperate nearshore ecosystems that grows with a circannual ‘stop-start’ pattern. Species of Laminaria play important ecological roles in kelp forests worldwide and are harvested commercially as a source of food and valuable extracts. In order to evaluate seasonal differences in tissue properties and composition, we compared the material properties, histology and cell-wall composition of overwintering blades with newly synthesized, actively growing blades from Laminaria setchellii. We found that overwintering blades were fortified with a thicker cortex and increased cell wall investment, leading to increased material strength. Overwintering tissues were composed of higher proportions of cellulose and fucose-containing polysaccharides (i.e. FCSPs, fucoidans) than newly formed blades and were found to possess thicker cell walls, likely to withstand the waves of winter storms. Chemical cell wall profiling revealed that significant proportions of fucose were associated with cellulose, especially in overwintering tissues, confirming the association between cellulose and some fucose-containing polysaccharides. Changes in material properties during the resting phase may allow these kelps to retain their non-growing blades through several months of winter storms. The results of this study demonstrate how one species might regulate its material properties seasonally, and at the same time shed light on the mechanisms that might control the material properties of kelps in general.
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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".