First in situ estimations of small phytoplankton carbon and nitrogen uptake rates in the Kara, Laptev, and East Siberian seas
Bibliographic record
Abstract
Abstract. Carbon and nitrogen uptake rates by small phytoplankton (0.7–5 μm) in the Kara, Laptev, and East Siberian seas in the Arctic Ocean were quantified using in situ isotope labelling experiments for the first time as part of the NABOS (Nansen and Amundsen Basins Observational System) program during August 21 to September 22, 2013. The depth integrated C, NO3−, and NH4+ uptake rates by small phytoplankton showed a wide range from 0.54 to 15.96 mg C m−2 h−1, 0.05 to 1.02 and 0.11 to 3.73 mg N m−2 h−1, respectively. The contributions of small phytoplankton towards the total C, NO3−, and NH4+ was varied from 24 to 89 %, 32 to 89 %, and 28 to 89 %, respectively. The turnover times for NO3− and NH4+ by small phytoplankton during the present study point towards the longer residence times (years) of the nutrients in the deeper waters, particularly for NO3−. Relatively, higher C and N uptake rates by small phytoplankton obtained during the present study at locations with less sea ice concentrations points towards the possibility of small phytoplankton thrive under sea ice retreat under warming conditions. The high contributions of small phytoplankton towards the total carbon and nitrogen uptake rates suggest capability of small size autotrophs to withstand in the adverse hydrographic conditions introduced by climate change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".