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Record W2169000994 · doi:10.1897/04-575r.1

Copper inhibition of soil organic matter decomposition in a seventy-year field exposure

2006· article· en· W2169000994 on OpenAlexaff
Sébastien Sauvé

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsOrganic matterEnvironmental scienceEnvironmental chemistrySoil organic matterDecompositionSoil respirationSoil qualitySoil carbonSoil contaminationSoil waterSoil PollutantsSoil scienceChemistry

Abstract

fetched live from OpenAlex

On a site contaminated decades ago with Cu from a wood treatment facility, we can observe that the decomposition of soil organic matter has been slowed. This represents an exceptional data set, and it allows us to address many challenges faced by regulators and risk assessors who are trying to derive appropriate soil quality criteria. These data are representative of a field study with a very well-equilibrated contamination and allow the derivation of chronic toxicity threshold values for the inhibition of microbial respiration. Soil respiration is the main determinant of the carbon balance, and it is assessed using the accumulation of soil organic matter (SOM) in this case. Using data derived from a 70-year-old field study also has the advantage of not being subject to risk assessment uncertainty factors arising from the potential aging effects of spiked soil or to the uncertainty caused by laboratory-to-field differences, both of which are very difficult to address. The resulting toxicity thresholds for an inhibition of SOM degradation are 154, 193, and 285 mg Cu/kg dry soil for inhibition levels of 10, 20, and 50%, respectively. Setting those thresholds correctly is critical for a proper risk assessment relative to sustainable development and agriculture.

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 categoriesInsufficient payload (model declined to judge)
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 score0.077
Threshold uncertainty score0.986

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)0.0150.000

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 head0.003
GPT teacher head0.193
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.

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

Citations36
Published2006
Admission routes1
Has abstractyes

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