PARTNERS Project Arctic River Biogeochemical Data
Bibliographic record
Abstract
As the precursor to the Arctic Great Rivers Observatory projects (Arctic-GRO), the PARTNERS project (NSF-OPP-0229302) was one of 18 proposals funded in 2002 in response to the National Science Foundation - Arctic System Science (NSF-ARCSS) Arctic Freshwater Cycle: Land/Upper-Ocean Linkages solicitation. The PARTNERS project focused on the export and fate of water and water-borne constituents from the pan-Arctic watershed, through the establishment of major field sampling programs at downstream stations on the six largest rivers within the pan-Arctic domain; the Yenisey, Ob', Lena, and Kolyma Rivers in Siberia and the Yukon and Mackenzie Rivers in North America. The project established and implemented a set of standard protocols across all rivers, and undertook directed sample collections during the spring freshet, and winter (under-ice) periods, in addition to the more commonly-sampled winter months. As a result, the PARTNERS framework, and data that it generated, enabled a significant step forward in our understanding of large, Arctic rivers. International collaborations were, and continue to be, key to the success of this work. In this collaborative spirit, the initial project was named PARTNERS (Pan-Arctic River Transport of Nutrients, Organic Matter and Suspended Sediments) although many more constituents and isotopes were collected than this name implies. Biogeochemical data generated during the PARTNERS project are provided here. Data generated during the subsequent Arctic GRO projects are provided in sister pages on the Arctic Data Center site.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.026 |
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".