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Record W2612351686 · doi:10.1007/s10584-017-1977-1

Soil temperature responses to climate change along a gradient of upland–riparian transect in boreal forest

2017· article· en· W2612351686 on OpenAlexaff
S. K. Oni, F. Mieres, Martyn N. Futter, Hjalmar Laudon

Bibliographic record

VenueClimatic Change · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsTrent University
FundersSvenska Forskningsrådet FormasKempe FoundationKempestiftelsernaStiftelsen för Miljöstrategisk Forskning
KeywordsRiparian zoneSoil waterEnvironmental scienceTransectClimate changeTaigaBorealRiparian forestHydrology (agriculture)EcosystemSoil scienceEcologyGeologyGeographyForestryOceanography

Abstract

fetched live from OpenAlex

There is growing evidence of climate change impacts on northern ecosystems. While most climate change studies base their assessments on air temperature, spatial variation of soil temperature responses have not been fully examined as a metric of climate change. Here we examined spatial variations of soil temperature responses to an ensemble of regional climate model (RCM) projections at multiple depths in upland and riparian zones in the Swedish boreal forest. Modeling showed a stronger influence of air temperature on riparian soil temperature than was simulated for upland soils. The RCM ensemble projected a warming range of 4.7–6.0 °C in riparian and 4.3–5.7 °C in upland soils. However, soils were slightly colder in the riparian zone during winter. While the historical record showed that upland soils are about 0.4 °C warmer than the riparian soils, this may be reversed in the future as model projections showed that on an annual basis, riparian soils might be slightly warmer by 0.2 to 0.4 °C than upland soils. However, upland soils could warm up earlier (April) compared to riparian soils (May).

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 score0.061
Threshold uncertainty score0.121

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 head0.094
GPT teacher head0.289
Teacher spread0.194 · 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

Citations22
Published2017
Admission routes1
Has abstractyes

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