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Record W2844611649 · doi:10.1029/2018gl079147

Impacts of 1.5 and 2.0 °C Warming on Pan‐Arctic River Discharge Into the Hudson Bay Complex Through 2070

2018· article· en· W2844611649 on OpenAlexafffund
Matthew K. MacDonald, Tricia Stadnyk, Stephen J. Déry, Marco Braun, David Gustafsson, Kristina Isberg, Berit Arheimer

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsOuranosUniversity of ManitobaUniversity of Northern British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBayEnvironmental scienceDischargeGlobal warmingArcticPrecipitationClimatologyOceanographySpring (device)Climate changeDrainage basinGeologyGeographyMeteorology

Abstract

fetched live from OpenAlex

Abstract Discharge projections into the Hudson Bay Complex to 2070 are investigated for global mean temperature warming levels of 1.5 and 2.0 °C. Median precipitation increases from 1986–2005, ranging from 2% during summer to 19% during winter, are projected to increase discharge in all seasons except summer. The rise in discharge is greatest furthest north, into Foxe Basin, Ungava Bay, and Hudson Strait, exceeding 10% above historical annual means. A 2.0 °C warming results in higher discharge than 1.5 °C warming owing to greater precipitation (e.g., 6.5% greater spring discharge increase); however, summer discharge for 2.0 °C warming is lower due to enhanced evaporation and lower precipitation increase from historical (4.0% lower summer discharge increase). Extreme daily high flows are projected to be greater than historical, more so for 2.0 °C warming than 1.5 °C warming, and this is greatest in the eastern and northern regions. These projections suggest continued increasing river discharge into pan‐Arctic coastal oceans.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.460

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.312
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations39
Published2018
Admission routes2
Has abstractyes

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