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Record W3183901350

Physical Soil Disturbance Effects On Soil pH In The Greater Vancouver Area

2020· article· en· W3183901350 on OpenAlexaboutno aff
Fernando Alain Incio Flores, Trang Huynh, K. Saller, M. Zhu

Bibliographic record

VenueExpedition · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsDisturbance (geology)Soil pHSoil testEnvironmental scienceSoil scienceNull hypothesisEcologyGeographySoil waterMathematicsGeologyBiologyStatistics
DOInot available

Abstract

fetched live from OpenAlex

As human settlement is expanding, and agricultural practices are becoming increasinglydetrimental to the soil to cope with the increasing demand of food, it’s important to understandthe impact of physical disturbance on soil. Soil pH level varies across a landscape and isdependent on microbial and fungal content, as well as the type of disturbance it experiences. Thisstudy investigates the relationship between soil pH level and its physical disturbance, andhypothesizes an increase in soil acidification as soil disturbance increases. Across the GreaterVancouver Area, Canada, 48 soil samples were obtained from four different classes of soildisturbance, whereby the pH of each soil sample was recorded with chemical pH test kits.Following this, a one-way ANOVA test was carried out to assess the means between differentsoil classes, resulting in a p value of 0.054. As a consequence, it was concluded that the meanswere not statistically significant, thus failing to reject the null hypothesis. However, as the pvalueindicated that the means were not statistically different, it was concluded that theexperiment needs to be replicated with a larger sample size to obtain a clearer interpretation ofthe results.

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 score0.741
Threshold uncertainty score0.124

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 head0.015
GPT teacher head0.203
Teacher spread0.188 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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