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Record W4404862497 · doi:10.5194/bg-21-5381-2024

Evaluating adsorption isotherm models for determining the partitioning of ammonium between soil and soil pore water in environmental soil samples

2024· article· en· W4404862497 on OpenAlexafffundabout
Matthew Gordon Davis, Kevin Yan, J. G. Murphy

Bibliographic record

VenueBiogeosciences · 2024
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change CanadaUniversity of Toronto
KeywordsAdsorptionAmmoniumEnvironmental scienceEnvironmental chemistrySoil scienceSoil waterChemistry

Abstract

fetched live from OpenAlex

Abstract. Ammonium in soil pore water is thought to participate in bidirectional exchange with the atmosphere; however, common soil nutrient analysis methods determine the bulk quantity of ammonium associated with the soil particles rather than determining the aqueous ammonium concentration. Previous works have applied the Langmuir and Freundlich isotherm equations to ammonium-enriched soils to estimate partitioning, but this may not be representative of conditions in natural, unmanaged soils. In this work, environmental soil samples were collected from green spaces in Toronto and used to evaluate several commonly used adsorption isotherm equations, including the Langmuir, Freundlich, Temkin and Toth equations, to determine their applicability in lightly managed and non-fertilized soils. We then compare ammonia emission potentials (a quantity predicting the propensity of ammonia to volatilize from a liquid reservoir) determined using a conventional high-salt extraction procedure to determine the soil ammonium content to that modelled using the Temkin and Langmuir equations and demonstrate that conventional approaches may overestimate emission potentials from soils by a factor of 5–20.

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 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 score0.103
Threshold uncertainty score0.337

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)0.0000.000

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 head0.101
GPT teacher head0.335
Teacher spread0.233 · 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

Citations3
Published2024
Admission routes3
Has abstractyes

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