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Record W2512443396 · doi:10.1002/2016wr019436

Isoscapes of δ<sup>18</sup>O and δ<sup>2</sup>H reveal climatic forcings on Alaska and Yukon precipitation

2016· article· en· W2512443396 on OpenAlexaboutno aff
Matthew S. Lachniet, Daniel E. Lawson, Haroon Stephen, Alison Sloat, William P. Patterson

Bibliographic record

VenueWater Resources Research · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostPaleoclimatologyArcticHoloceneGeologyOrographic liftPrecipitationStable isotope ratioIce coreMeteoric waterPhysical geographyClimatologyAtmospheric sciencesOceanographyClimate changeGeographyGroundwaterMeteorology

Abstract

fetched live from OpenAlex

Abstract Spatially extensive Arctic stable isotope data are sparse, inhibiting the climatic understanding required to interpret paleoclimate proxy records. To fill this need, we constrained the climatic and physiographic controls on δ18O and δD values of stream waters across Alaska and the Yukon to derive interpolated isoscape maps. δ18O is strongly correlated to winter temperature parameters and similarity of the surface water line (δ2H = 8.0 × δ18O + 6.4) to the Global Meteoric Water Line suggests stream waters are a proxy for meteoric precipitation. We observe extreme orographic δ18O decreases and a trans‐Alaskan continental gradient of −8.3‰ 1000 km−1. Continental gradients are high in coastal zones and low in the interior. Localized δ18O increases indicate inland air mass penetration via topographic lows. Using observed δ18O/temperature gradients, we show that δ18O decreases in a ∼24 ka permafrost ice wedge relative to the late Holocene indicate mean annual and coldest quarter temperature reductions of 8.9 ± 1.7°C and 17.2 ± 3.2°C, respectively.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.978
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

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

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 head0.067
GPT teacher head0.300
Teacher spread0.232 · 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 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

Citations47
Published2016
Admission routes1
Has abstractyes

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