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Record W2487295547 · doi:10.2136/sssaspecpub57.2ed.c13

Soil Organic Carbon Sequestration by Biochemically Recalcitrant Biomacromolecules

2009· book-chapter· en· W2487295547 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSSSA special publication series · 2009
Typebook-chapter
Languageen
FieldEngineering
TopicPolymer-Based Agricultural Enhancements
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsCarbon sequestrationSoil organic matterSoil carbonEnvironmental chemistryChemistrySoil biologySoil waterOrganic matterEnvironmental scienceLigninCarbon fibersSoil scienceCarbon dioxideMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

This chapter focuses on carbon sequestration by managing biochemical recalcitrant soil organic carbon (SOC) fractions. This is a potential but overlooked strategy to offset CO2 emissions to mitigate climate change. The chapter compares biochemically recalcitrant biomacromolecules in plants, soil microorganisms, and soil fauna as potential precursors of recalcitrant SOC fractions. Then, it discusses chemical structures of biochemically recalcitrant SOC fractions visible by modern analytical methods. In soils, biochemically recalcitrant molecules accumulate in soil organic matter (SOM) because they are strongly resistant to decomposition. In particular, polymethylenic molecules, but less lignin-derived C, contribute to the stable SOC pool in deeper soil horizons. Yet, people must identify terrestrial C sequestration strategies to enhance inputs from aliphatic and other recalcitrant compounds at deeper soil depth, where turnover rates of SOM are lower than in surface horizons.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.320
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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