Modelling the impact of riverine DON removal by marine bacterioplankton on primary production in the Arctic Ocean
Bibliographic record
Abstract
Abstract. The planktonic and biogeochemical dynamics of the Arctic shelves exhibit a strong variability in response to Arctic warming. In this study, we employ a biogeochemical model coupled to a pan-Arctic ocean–sea ice model (MITgcm) to elucidate the processes regulating the primary production (PP) of phytoplankton, bacterioplankton (BP), and their interactions. The model explicitly simulates and quantifies the contribution of usable dissolved organic nitrogen (DON) drained by the major circum-Arctic rivers to PP and BP in a scenario of melting sea ice (1998–2011). Model simulations suggest that, on average between 1998 and 2011, the removal of usable riverine dissolved organic nitrogen (RDON) by bacterioplankton is responsible for a ~ 26% increase in the annual BP for the whole Arctic Ocean. With respect to total PP, the model simulates an increase of ~ 8% on an annual basis and of ~ 18% in summer. Recycled ammonium is responsible for the PP increase. The recycling of RDON by bacterioplankton promotes higher BP and PP, but there is no significant temporal trend in the BP : PP ratio within the ice-free shelves over the 1998–2011 period. This suggests no significant evolution in the balance between autotrophy and heterotrophy in the last decade, with a constant annual flux of RDON into the coastal ocean, although changes in RDON supply and further reduction in sea-ice cover could potentially alter this delicate balance.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".