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Record W2969928601 · doi:10.1073/pnas.1904241116

Proglacial freshwaters are significant and previously unrecognized sinks of atmospheric CO <sub>2</sub>

2019· article· en· W2969928601 on OpenAlexafffundabout
Kyra A. St. Pierre, Vincent L. St. Louis, Sherry L. Schiff, Igor Lehnherr, Paul G. Dainard, Alex Gardner, Pieter J. K. Aukes, Martin Sharp

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of TorontoUniversity of WaterlooUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaArcticNet
KeywordsWeatheringGlacierGlacial periodWatershedEnvironmental scienceSink (geography)ArcticFreshwater ecosystemEcosystemDissolved organic carbonCarbon sinkPhysical geographySTREAMSBiogeochemistryEarth scienceCarbon cycleLimnologyHydrology (agriculture)Climate changeGeologyOceanographyEcologyGeomorphologyGeography

Abstract

fetched live from OpenAlex

Significance Glacier melt is one of the most dramatic consequences of climate change in high-latitude and high-altitude environments. As meltwaters move across poorly consolidated landscapes, they transport vast quantities of highly reactive comminuted sediments prone to chemical weathering reactions that may consume atmospheric CO 2 . Using a whole watershed approach in the Canadian High Arctic, combined with additional dissolved CO 2 measurements in glacial rivers in Greenland and the Canadian Rockies, we show that certain glacier-fed freshwater ecosystems are significant and previously unrecognized annual CO 2 sinks due to chemical weathering. As many of the world’s rivers originate from glacial headwaters, we highlight the potential importance of this process for contemporary regional carbon budgets in rapidly changing high-latitude and high-altitude watersheds.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.240
Teacher spread0.214 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations79
Published2019
Admission routes3
Has abstractyes

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