Primary Production in the Kara, Laptev, and East Siberian Seas
Bibliographic record
Abstract
Understanding of the primary production of phytoplankton in the Kara Sea (KS), the Laptev Sea (LS), and the East Siberian Sea (ESS) remains limited, despite the recognized importance of phytoplankton in the Arctic Ocean. To address this knowledge gap, we conducted three NABOS (Nansen and Amundsen Basins Observational System) expeditions in 2013, 2015, and 2018 to measure in situ primary production rates using a 13C-15N dual-tracer method and examine their major controlling factors. The main goals in this study were to investigate regional heterogeneity in primary production and derive its contemporary ranges in the KS, LS, and ESS. The daily primary production rates in this study (99 ± 62, 100 ± 77, and 56 ± 35 mg C m−2 d−1 in the KS, LS, and ESS, respectively) are rather different from the values previously reported in each sea mainly because of spatial and regional differences. Among the three seas, a significantly lower primary production rate was observed in the ESS in comparison to those in the KS and LS. This is likely mainly because of regional differences in freshwater content based on the noticeable relationship (Spearman, rs = −0.714, p < 0.05) between the freshwater content and the primary production rates observed in this study. The contemporary ranges of the annual primary production based on this and previous studies are 0.96–2.64, 0.72–50.52, and 1.68–16.68 g C m−2 in the KS, LS, and ESS, respectively. Further intensive field measurements are warranted to enhance our understanding of marine microorganisms and their community-level responses to the currently changing environmental conditions in these poorly studied regions of the Arctic Ocean.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".