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Record W4364378817 · doi:10.1177/03091333231169431

Role of Andean tropical montane soil organic carbon in the deglacial carbon budget

2023· article· en· W4364378817 on OpenAlexaff
Nicholas S. Bill

Bibliographic record

VenueProgress in Physical Geography Earth and Environment · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsDeglaciationSoil carbonLast Glacial MaximumEnvironmental scienceCarbon cycleMontane ecologyThinningTotal organic carbonGeologySoil waterPhysical geographyEarth scienceClimatologyGlacial periodEcologyEcosystemSoil scienceGeomorphologyGeographyForestry

Abstract

fetched live from OpenAlex

During the last deglaciation, atmospheric CO 2 increased by about 75 ppm. The deep ocean is likely the dominant source of this atmospheric CO 2 rise in the atmospheric pool; however, a consensus accounting for the entire 75 ppm remains elusive. Since the deep ocean cannot account for the entire 75 ppm, the terrestrial environment likely makes up the remainder. This paper provides a mechanism for an unaccounted-for portion of the source of this terrestrial carbon, that being soil organic carbon (SOC) from the tropical montane Andes, and with that, minimum constraints on the contribution of SOC to the total rise in atmospheric CO 2 during the last deglaciation. Using numerical climate modeling input into an empirical model derived from tropical montane forests of the Andes Mountains in South America, this study finds that during the last deglaciation, the organic layer thickness was thinning from the Last Glacial Maximum (LGM) to the present (pre-industrial) in the tropical montane Andes. This overall warming and organic layer thickness thinning may have led to a loss of available carbon storage space, causing a leak of CO 2 into the atmosphere over this time scale. This study finds an estimate for the contribution of global atmospheric CO 2 from SOC in tropical montane Andean soils is likely at least ∼1.4 ppm CO 2 since the LGM.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.371

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 studies0.0000.001
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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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