A simple and effective protocol for cryopreservation of germplasm of the bull kelp, <i>Nereocystis luetkeana</i> (Phaeophyceae)
Bibliographic record
Abstract
Kelps are large brown seaweeds that can form three-dimensional underwater forests that provide food and habitat for a wide diversity of marine organisms. They also provide a wealth of ecosystem services to humans and may be able to help combat climate change through blue carbon. However, kelps are currently in decline in many parts of the world, most likely due to rising ocean temperatures, and conservation action is needed quickly to preserve kelp biodiversity. One kelp conservation strategy that needs further development is biobanking, the storage of biological material. In particular, the development of cryopreservation protocols would permit easier storage of large quantities of kelp germplasm under stable conditions. In this paper, we compare the effectiveness of different cryoprotective agents-chemicals that mitigate the damaging effects of freezing on living tissue-for use in cryopreservation of gametophyte tissue of the bull kelp, Nereocystis luetkeana. We observed that when cryopreserved in a solution of 10% ethylene glycol + 9% sorbitol, Nereocystis gametophytes of both sexes showed excellent survivorship 6 weeks after removal from cryogenic conditions. Although kelp cryopreservation protocols still need to be further researched, we believe that these methods have great potential to improve and expand kelp biobanking, and we would encourage the development of protocols for more kelp species as well as more widespread adoption of cryopreservation by existing kelp biobanking efforts.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".