Distribution, Sources, and Dynamics of Particulate Matter Along Trans-Arctic Sections
Bibliographic record
Abstract
In order to better understand the sources, sinks and hydrodynamic/biogeochemical influences on particulate matter distribution and variability in Arctic basins, we combined data from two 2015 fall expeditions: one from Bering Strait (USCGC Healy) and the other from Barents Sea (R/V Polarstern) meeting at the North Pole. Sections of beam attenuation due to particles were overlain by salinity, temperature, and chlorophyll-a fluorescence (Chl-a), and with nitrate contours on Chl-a sections to compare with concentrations of particulate matter (PM) and particulate organic carbon (POC) from full water column filtered samples. Dense Pacific water moving swiftly through Bering Strait erodes and carries sediment-laden waters onto the Chukchi Shelf, much of it moving in and above Barrow Canyon or is entrained in eddies. This nutrient-rich Pacific water sinks below the low-salinity, nutrient-poor polar mixed layer, forming a thick lens of high salinity water known as Pacific halocline waters. The nutrient-poor mixed layer inhibits photosynthesis in surface waters of Canada and Makarov Basins, but subsurface Chl-a maxima are observed when nutrients are available. Surface-water POC biomass appears greater in Barents Sea than in Beaufort Sea because nutrient-rich Atlantic water entering Barents Sea is not isolated from surface waters by strong stratification. Surface water freezes, creating high-density water that cascades into 400 m basins in Barents Sea and into deep Nansen Basin, eroding sediment that forms patches of nepheloid layers in the shallow basins. Nepheloid layers in the deep basins are very weak, consistent with a lack of strong currents there.
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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 | 0.001 | 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 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".