Terrestrial exports of dissolved and particulate organic carbon affect nearshore ecosystems of the Pacific coastal temperate rainforest
Bibliographic record
Abstract
Abstract Watersheds of the coastal temperate rainforests of Pacific North America export large amounts of organic carbon (OC) to the coastal ocean. While it has been suggested that terrestrially derived organic matter could subsidize marine food webs and affect ocean biogeochemistry along the coastal margin, little work has been done to quantify and characterize OC across the freshwater to marine continuum. We conducted monthly and targeted rainfall event surveys of dissolved and particulate organic carbon (DOC and POC) quantity and quality (δ13C, dissolved organic matter characterization) across a freshwater to marine salinity gradient between Calvert and Hecate Islands, British Columbia, Canada. Freshwater DOC concentrations (9.97 ± 0.25 mg L−1) far exceeded those in marine waters (1.24 ± 0.03 mg L−1), while POC concentrations were similar across all sites (0.23 ± 0.01 mg L−1). δ13C‐DOC and ‐POC in freshwaters were constant, but varied seasonally at the marine stations with freshwater and marine processes. Rainfall events facilitated the rapid export of terrestrial DOC and POC to coastal waters, altering water quality and potentially subsidizing microbial productivity across marine surface waters. On an annual basis, primary production in marine waters (21–42 Gg C) exceeded total freshwater OC contributions (1.8–2.2 Gg C); however, freshwater exports were more important during the autumn and winter months, when rainfall was highest and primary production was limited by shorter days and deep turbulent mixing. Our results highlight the importance of storms for connecting the coastal temperate rainforest with surface coastal waters, especially during the summer when connectivity between the freshwater and marine ecosystems is otherwise low.
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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 | 0.001 | 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".