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Record W2083647811 · doi:10.1139/s04-081

Effect of forest harvest on soil temperature and water storage and movement patterns on Boreal Plain hillslopes

2005· article· en· W2083647811 on OpenAlexfundvenueaboutno aff
I R Whitson, D. S. Chanasyk, Ellie E. Prepas

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2005
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
FundersIndustry Canada
KeywordsInterflowEnvironmental scienceWater contentSoil waterHydrology (agriculture)Water storageTaigaSnowmeltSoil scienceSurface runoffGeologyEcologyForestryOceanographyGeography

Abstract

fetched live from OpenAlex

Through its effect on vegetation, forest harvesting affects soil moisture storage, water movement processes, and soil temperature. The effects of harvest on water storage and movement patterns and on soil temperature during the snowmelt period were studied 3 years after harvest on hillslopes in northern Alberta, Canada. Measurements of soil water content, soil temperature, and bromide tracer concentrations were made at 11 pairs of forested and harvested plots, which are all dominated by Gray Luvisol soils but possess a range of topographic characteristics. Compared with forested sites, harvested sites were expected to have higher soil water content, display more interflow, and thaw earlier. Harvested sites were wetter on only one of four dates sampled, reflecting dry weather and rapid aspen regrowth. Greater loss and shallower penetration of bromide tracer in plots indicate that harvested sites had more interflow than forested sites. Despite a trend to higher temperatures, harvested sites did not thaw earlier than forested sites. Our inability to detect dramatic treatment effects reflects the need for large sample sizes for soil properties that display high local spatial and temporal variability. Key words: forest harvest, soil moisture, soil thaw, bromide, interflow, boreal.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.339

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 head0.002
GPT teacher head0.160
Teacher spread0.158 · 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 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

Citations16
Published2005
Admission routes3
Has abstractyes

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