A blueprint for national assessments of the blue carbon capacity of kelp forests applied to Canada’s coastline
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Kelp forests offer substantial carbon fixation, with the potential to contribute to natural climate solutions (NCS). However, to be included in national NCS inventories, governments must first quantify the kelp-derived carbon stocks and fluxes leading to carbon sequestration. Here, we present a blueprint for assessing the national blue carbon capacity of kelp forests in which data synthesis and Bayesian hierarchical modeling enable estimates of kelp carbon production, storage, and export capacity from limited data. Applying this blueprint to Canada’s extensive coastline, we estimate kelps hold 0.6 to 2.8 Tg C in short-term biomass, producing 1.1 to 6.2 Tg C yr -1 , of which 0.04 to 0.4 Tg C yr -1 could be exported to the deep ocean. While modest compared to terrestrial sinks, our findings suggest kelps have comparable carbon sequestration to marine and freshwater wetlands, warranting further consideration in Canada’s NCS inventories. Our transparent, reproducible blueprint represents an important step towards accurate carbon accounting for kelp forests.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it