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Record W2086554042 · doi:10.1038/npre.2009.3665.1

Five years of winter climate change-related research in the Canadian low Arctic: What have we learned?

2009· preprint· en· W2086554042 on OpenAlexaffabout
Paul Grogan, Kate M. Buckeridge

Bibliographic record

VenueNature Precedings · 2009
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsQueen's University
Fundersnot available
KeywordsTundraBiogeochemical cycleBiogeochemistryEnvironmental scienceEcosystemClimate changeGrowing seasonArcticSpring (device)SnowTerrestrial ecosystemPhysical geographyVegetation (pathology)EcologyGeography

Abstract

fetched live from OpenAlex

Abstract The importance of the fall, winter and spring periods to ecosystem functioning and biogeochemical cycling in tundra has only become apparent in the past two decades. Our research group has been conducting winter climate change-related studies at a low arctic tundra site near Daring Lake, north of Yellowknife in northern Canada for the past five years. Most of these studies have focused on the biogeochemical interactions between plants, soils, and soil microbes during fall, winter and spring, and on their responses to experimentally deepened snow. In addition, we have measured trace gas production and isotopic nitrogen tracer distributions among plant and soil components in several vegetation-types. The central goal has been to understand the potential importance of cold season soil N transformation processes to ecosystem-level biogeochemistry during the subsequent plant growing season, and then to develop predictions of how changes in winter climate may impact these seasonal processes. In this talk, I will present a synthesis of those studies, emphasizing temperature-moisture interactions, and highlighting future research priorities.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.005
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 head0.085
GPT teacher head0.336
Teacher spread0.251 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2009
Admission routes2
Has abstractyes

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