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Record W1970136000 · doi:10.1002/ppp.627

Temperature and moisture trends in non‐sorted earth hummocks and stripes on the Old Man Range, New Zealand: implications for mechanisms of maintenance

2008· article· en· W1970136000 on OpenAlexaff
Matthew B. Scott, Katharine J. M. Dickinson, B.I.P. Barratt, Brent J. Sinclair

Bibliographic record

VenuePermafrost and Periglacial Processes · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsWestern University
FundersCore Research for Evolutional Science and Technology
KeywordsWater contentLandformPermafrostGeologyMoistureRange (aeronautics)Udic moisture regimeTemperature gradientSoil scienceHydrology (agriculture)Atmospheric sciencesSoil waterEnvironmental scienceGeomorphologyOceanographyGeotechnical engineeringMeteorologyGeographyLoam

Abstract

fetched live from OpenAlex

Abstract The mechanisms of maintenance of earth hummocks and non‐sorted stripes in seasonally frozen ground on the Old Man Range, Otago, New Zealand were investigated. These landforms are hypothesised to be active periglacial landforms maintained by seasonal movement of moisture down an energy gradient. We tested three hypotheses: 1) freezing should occur predominantly in the crests of the stripes and hummocks; 2) differential freezing patterns should be consistent between years; and 3) in the presence of a temperature gradient (i.e. in winter), soil moisture content should be greater in crests than in furrows. An array of 39 thermistors at each of two sites was used to monitor soil temperature gradients during three winters, and replicate soil cores were taken in autumn and winter to determine soil moisture gradients. Freezing occurred mainly in the crests and sides (heavily influenced by aspect), and patterns of freezing showed strong interannual consistency, supporting our first two hypotheses. Soil in crests had a higher moisture content in both freezing and thawed seasons, which was inconclusive relative to the expected water gradient. Copyright © 2008 John Wiley & Sons, Ltd.

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)0.000
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 score0.039
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.028
GPT teacher head0.246
Teacher spread0.218 · 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

Citations16
Published2008
Admission routes1
Has abstractyes

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