Arctic Great Rivers Observatory IV Biogeochemistry and Discharge Data: 2020-2024
Bibliographic record
Abstract
The PARTNERS (Pan-Arctic River Transport of Nutrients, Organic Matter, and Suspended Sediments) and Arctic-GRO (Arctic Great Rivers Observatory) projects sample the biogeochemistry of the six largest rivers draining to the Arctic Ocean: the Yenisey, Ob', Lena, and Kolyma Rivers in Siberia and the Yukon and Mackenzie Rivers in North America. To the greatest extent possible, sample collection techniques are identical across rivers. Once collected, samples are returned to Woods Hole, Massachusetts, from where they are shipped to expert laboratories for analyses. The Arctic Great Rivers Observatory IV (Arctic-GRO IV) Project spans the years between 2020 and 2024, and continues a sample collection effort that has been ongoing since 2004. On each river, samples are collected bi-monthly (six times per year), with target sampling months alternating between years. For real-time updates of the Arctic GRO dataset, please visit www.arcticgreatrivers.org/data.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.032 |
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".