A 50 % increase in the mass of terrestrial particles delivered by the Mackenzie River into the Beaufort Sea (Canadian Arctic Ocean) over the last 10 years
Bibliographic record
Abstract
Abstract. Global warming has a significant impact on the regional scale on the Arctic Ocean and surrounding coastal zones (i.e., Alaska, Canada, Greenland, Norway and Russia). The recent increase in air temperature has resulted in increased precipitation along the drainage basins of Arctic rivers. It has also directly impacted land and seawater temperatures with the consequence of melting permafrost and sea ice. An increase in freshwater discharge by main Arctic rivers has been clearly identified in time series of field observations. The freshwater discharge of the Mackenzie River has increased by 25% since 2003. This may have increased the mobilization and transport of various dissolved and particulate substances, including organic carbon, as well as their export to the ocean. The release from land to the ocean of such organic material, which has been sequestered in a frozen state since the Last Glacial Maximum, may significantly impact the Arctic Ocean carbon cycle as well as marine ecosystems. In this study we use 11 years of ocean color satellite data and field observations collected in 2009 to estimate the mass of terrestrial suspended solids and particulate organic carbon delivered by the Mackenzie River into the Beaufort Sea (Arctic Ocean). Our results show that during the summer period, the concentration of suspended solids at the river mouth, in the delta zone and in the river plume has increased by 46, 71 and 33%, respectively, since 2003. Combined with the variations observed in the freshwater discharge, this corresponds to a more than 50% increase in the particulate (terrestrial suspended particles and organic carbon) export from the Mackenzie River into the Beaufort Sea.
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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 | 0.001 | 0.001 |
| 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".