Ripped and torn off: insights into autumn carbon loss in shallow subarctic sugar kelp
Bibliographic record
Abstract
Kelp forests, recently recognized for their potential to capture and store carbon, remain relatively underexplored in their actual contributions to carbon budgets. Here, we investigated the carbon release dynamics of the sugar kelp, Saccharina latissima , a dominant kelp species of shallow subarctic zones, which is seasonally impacted by ice scour. Our study focused on autumn and winter, combining field assessments with laboratory experiments to quantify and compare mechanisms of biomass and carbon loss, and thus contributing new insights into seasonal kelp-derived carbon flows in these environments. Daily dislodgement rates were comparable to temperate regions, with higher rates in autumn than winter, contrary to our initial hypothesis about ice scouring effects. Apical blade erosion rates aligned with previous regional studies but were lower than temperate sites, possibly due to reduced bryozoan encrustation and colder waters. Laboratory measurements revealed higher dissolved organic carbon (DOC) release compared to other kelp species, while particulate organic carbon (POC) loss was 8-fold higher than DOC release. This study underscores the need for comprehensive annual measurements to refine carbon budgets, especially in subarctic kelp forests, where carbon contributions are likely underestimated, particularly in shallow, seasonally dynamic environments.
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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 | 0.001 | 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".