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Record W2129605207 · doi:10.19189/001c.128487

Towards Robust Subsidence-Based Soil Carbon Emission Factors for Peat Soils in South-East Asia, With Special Reference to Oil Palm Plantations

2013· article· en· W2129605207 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMires and Peat · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
FundersInstitut Penyelidikan dan Kemajuan Pertanian MalaysiaUniversity of LeicesterAlberta Innovates - Health Solutions
KeywordsPeatSoil waterEnvironmental sciencePalm oilSubsidencePalmCarbon fibersSoil carbonSoil classificationGeologyAgroforestrySoil scienceGeographyGeomorphologyStructural basin

Abstract

fetched live from OpenAlex

Oil palm and Acacia pulpwood plantations are being established at a rapid rate on drained peatland in south-east Asia. Accurate measurements of associated carbon losses are still scarce, however, due mainly to difficulties of excluding autotrophic carbon fluxes from chamber-based flux measurements and uncertainties about the extent of waterborne losses. Here, we demonstrate a simple approach to determining total net carbon loss from subsidence records that is applicable to steady state conditions under continuous land use. We studied oil palm and Acacia plantations that had been drained for 5–19 years. Very similar subsidence rates and dry bulk density profiles were obtained, irrespective of crop type or age of the plantation, indicating that the peat profiles were in a steady state. These are conditions that allow for the deduction of net carbon loss by multiplying the rate of subsidence by the carbon density of the peat below the water table. With an average subsidence rate of 4.2 cm y - 1 and a carbon density of 0.043 g cm -3 , we arrive at a net carbon loss of ~18 t ha -1 y -1 (~66 t CO 2 -eq ha -1 y -1 ) for typical oil palm and Acacia plantations more than five years after drainage, without large differences between the plantation types. The proposed method enables calculation of regional or project-specific carbon loss rates to feed into mitigation schemes of the UN Framework Convention on Climate Change.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.994

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)0.0010.000

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 head0.024
GPT teacher head0.224
Teacher spread0.200 · 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