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Record W4311680980 · doi:10.22215/etd/2022-15158

Investigation of Daily to Seasonal Variation in Greenhouse Gas Emission and Cycling in Agricultural Riparian Zone Soils

2022· dissertation· en· W4311680980 on OpenAlexaffabout
Mitchell Richardson

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsCarleton University
Fundersnot available
KeywordsEnvironmental scienceRiparian zoneSoil waterGreenhouse gasWater contentHydrology (agriculture)AgricultureAtmospheric sciencesSoil scienceGeographyEcologyGeology

Abstract

fetched live from OpenAlex

Agricultural riparian zone microecosystems provide opportunity for mitigation of pollution transport and greenhouse gas emissions.In order to make recommendations to farmers as to best management practices, temporal variations in gas fluxes between the soil and atmosphere must be considered, and the controls on soil-gas behaviour must be better understood.In this study, CO2, O2, CH4, and N2O subsurface concentrations and surface fluxes were monitored with an average temporal resolution of 4 hours, along with soil temperature, soil moisture content, and barometric pressure from the beginning of May until the end of November 2021 at an active, arborous, agricultural riparian zone in St. Albert, Ontario.The results show varying control of barometric pressure, soil temperature, and moisture content on short-term changes in soil gas concentrations and emissions depending on the overall environmental conditions under which these changes in controlling parameters occur.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.309
Threshold uncertainty score0.615

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes2
Has abstractyes

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