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Record W1921648414 · doi:10.1029/2009wr008070

Effects of snow cover on soil freezing, water movement, and snowmelt infiltration: A paired plot experiment

2010· article· en· W1921648414 on OpenAlexaff
Yukiyoshi Iwata, Masaki Hayashi, Shinji Suzuki, Tomoyoshi Hirota, Shuichi Hasegawa

Bibliographic record

VenueWater Resources Research · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSnowmeltSurface runoffInfiltration (HVAC)Environmental scienceSnowFrost (temperature)MeltwaterHydrology (agriculture)Soil waterFrost heavingSoil scienceGeologyGeotechnical engineeringEcologyMeteorologyGeomorphology

Abstract

fetched live from OpenAlex

A dramatic reduction in soil frost depth has been reported for Hokkaido Island of northern Japan over the last 20 years. Since soil frost strongly affects snowmelt infiltration and runoff, the reduction in frost depth may have altered the water and nutrient cycles in this region. A paired‐plot experiment was conducted in an agricultural field in Tokachi, Hokkaido, to compare the movement of soil water at different frost depths, controlled by manipulating the depth of snow cover. Snow was removed to enhance soil freezing in the treatment plot and was undisturbed in the control plot. The soil froze to a maximum depth of 0.43 m under the treatment plot and 0.11 m under the control plot. During the freezing period, the amount of upward soil water flux toward the freezing front in the treatment plot was more than double that in the control plot. During the snowmelt period, infiltration of meltwater was unimpeded by the thin frozen layer in the control plot, whereas the relatively thick frozen layer in the treatment plot impeded infiltration and generated 63 mm of runoff. These results clearly show that the changes in the timing and thickness of snow cover deposition can cause a dramatic reduction of frost depth and change in the soil water dynamics.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0080.001

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 head0.047
GPT teacher head0.289
Teacher spread0.241 · 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.

Study designBench or experimental
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

Citations184
Published2010
Admission routes1
Has abstractyes

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