Subarctic sugar kelp (<i>Saccharina latissima</i>, Phaeophyceae) summer productivity and contribution to carbon budgets
Bibliographic record
Abstract
Kelp forests are known to be very productive ecosystems and constitute a central component of the marine carbon cycle in coastal areas. Nevertheless, crucial carbon-related data are missing to be able to include them properly in carbon budgets. A thorough understanding of the kelp contribution to the carbon cycle is especially important in regions prone to experiencing strong seasonal fluctuations in environmental conditions, such as subarctic regions. This study aimed to quantify primary productivity through growth rates and oxygen fluxes of a dominant kelp species in subarctic regions, Saccharina latissima, and to link oxygen fluxes to environmental parameters. Our results showed that strong primary productivity oxygen fluxes coincided with high light levels in July and most of August, while growth rates stayed similar all summer. An overall decline in all primary productivity proxies happened from late August, suggesting a seasonal slowing down of S. latissima metabolism. The estimated quantity of carbon stored in tissue during growth represented from 6% to 28% of the gross primary productivity. Further research is needed to explore how and how much carbon transits through living kelp tissue in different seasons, to better understand the contribution of subarctic kelp to coastal carbon budgets.
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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 | 0.001 |
| 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".