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Record W1963868408 · doi:10.2480/agrmet.65.1.2

Seasonal and Annual Water Balance of Agricultural Land in Tokachi, Hokkaido, Japan

2009· article· en· W1963868408 on OpenAlexaff
Tomoyoshi Hirota, Yukiyoshi Iwata, M. Nemoto, Takahiro Hamasaki, Ryoji Sameshima, Masaki Hayashi

Bibliographic record

VenueJournal of Agricultural Meteorology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSnowmeltSnowEnvironmental sciencePrecipitationWater balanceHydrometeorologyLeaching (pedology)Hydrology (agriculture)Period (music)Snow coverGroundwaterSoil waterAtmospheric sciencesClimatologySoil scienceGeologyMeteorologyGeography

Abstract

fetched live from OpenAlex

Soil water balance determines the accumulation and leaching of solutes in the surface soil. In cold regions, where agricultural land is covered with snow in winter, the soil water balance has not been as thoroughly investigated as in warm regions due to the difficulty of conducting continuous and accurate winter field observation in cold environments. In this study, annual water balance at Tokachi, Hokkaido, Japan was evaluated including snow-covered periods using a comprehensive hydrometeorological observation system developed for cold region application. During the period from October 20, 2004 to October 19, 2005, the ground was covered with snow for a total of 130 days. Annual precipitation during the study period was 799 mm and estimated evaporation was 591 mm. During the snow-covered period, precipitation was 260 mm, accounting for only 32.5% of the annual precipitation; however, precipitation excess was large (236 mm) due to very small evaporation (24 mm), and the bulk of it infiltrated rapidly during the four-week snowmelt period. This observation suggests that the leaching of soil solutes occurs almost exclusively during the snowmelt period, and that the agricultural soil environment in this region is strongly affected by the water balance in the snow-cover period.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.287

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 head0.004
GPT teacher head0.194
Teacher spread0.191 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

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