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Record W4327702730 · doi:10.1139/cjfr-2022-0226

Including soil alters the optimization of forestry with carbon sinks

2023· article· en· W4327702730 on OpenAlexvenueno aff
Vesa-Pekka Parkatti, Olli Tahvonen, Toni Viskari, Jari Liski

Bibliographic record

VenueCanadian Journal of Forest Research · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
FundersBusiness Finland
KeywordsCarbon sinkSoil carbonEnvironmental scienceSink (geography)Carbon fibersThinningCarbon sequestrationForestryAgronomyAgroforestrySoil waterEcologySoil scienceMathematicsClimate changeCarbon dioxideBiologyGeography

Abstract

fetched live from OpenAlex

We integrate a carbon net sink and stand-level wood production to analyze their simultaneous optimization as an economic problem. Carbon is included in living trees, wood products, and forest soil. Forestry is specified by a size-structured model for optimizing thinning timing and intensity, rotation period, and the optimal choice of rotation versus continuous cover forestry. The optimal inclusion of a carbon net sink increases the carbon pool mainly in living trees and forest soil, while the effect on the product carbon pool remains minor. With a 3% interest rate, increasing the CO 2 price to €40 per tCO 2 increases the total steady-state carbon pool by 131% and the soil carbon accounts for ca. 60% of the increased carbon storage. Omitting soil carbon, as in previous studies, leads to underestimates of the carbon sink, significantly decreasing the optimal total CO 2 net sink and achievable economic net gain from joint wood production and carbon management. The inclusion of soil carbon suggests that, in contrast to previous results, a higher CO 2 price does not necessarily favor continuous cover forestry.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.053
GPT teacher head0.307
Teacher spread0.254 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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