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Record W6926016695 · doi:10.18739/a2xw47x7d

Arctic Great Rivers Observatory IV Biogeochemistry and Discharge Data: 2020-2024

2022· dataset· en· W6926016695 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueUC Santa Barbara · 2022
Typedataset
Languageen
FieldHealth Professions
TopicNursing care and research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiogeochemistryArcticObservatoryThe arcticPermafrostHydrology (agriculture)

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.098
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0990.001

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.

Opus teacher head0.094
GPT teacher head0.424
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it