Arctic Great Rivers Observatory III Biogeochemistry and Discharge Data, 2017-2019
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The PARTNERS (Pan-Arctic River Transport of Nutrients, Organic Matter, and Suspended Sediments) and Arctic-GRO (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, MA, from where they are shipped to expert laboratories for analyses. The Arctic Great Rivers Observatory III (Arctic-GRO III; NSF-OPP-1602615) Project spans the years between 2017 and 2019, 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. Full field and laboratory details for Arctic GRO III can be found in the "Sample Collection and Analyses" document on this page. For real-time updates of the Arctic GRO dataset, please visit www.arcticgreatrivers.org/data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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 it