Impact de la fonte de la glace sur le phytoplancton et le CO2 de l'Océan Arctique
Bibliographic record
Abstract
The most abrupt changes in response to climate change are observed in the Arctic Ocean. The high sensitivity of this polar environment was revealed by the exceptional sea ice reduction in recent summers. What is in turn the impact of ice melting on phytoplankton and atmospheric CO2 uptake? To address this question data were acquired during two oceanographic cruises carried out in the Western Arctic, during two summers 2008 and 2010 of extensive ice melting. Several parameters were used to characterize the physical environment (temperature, salinity, sea ice cover) and conditions for phytoplankton growth (light, nutrients). Primary production, pigments and taxonomy data were generated to describe and quantify the phytoplankton communities. Carbonate chemistry, hydrology and phytoplankton observations were combined to estimate the impact of ice melting on atmospheric CO2 uptake. Sub-regions were distinguished based on their sea ice cover, namely the ice edge, the ice-free deep basins and shelves and ice-covered areas where phytoplankton communities and carbonate chemistry differ. The role of freshening resulting from sea ice melting is evaluated from the biomass and phytoplankton community structure. We further investigated the impact of dilution and desalinization due to freshening on the CO2 uptake and acidification of the Arctic waters. Comparison to historical data documenting heavy ice years suggests that biomass, primary production, phytoplankton species and sewater CO2 are greatly modified in an Arctic Ocean affected by accelerated melting of ice.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".