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Record W1608159251

Carbon Sinks and the Preservation of Old-Growth Forests Under the Kyoto Protocol

2001· article· en· W1608159251 on OpenAlexaff
Dayna Nadine Scott

Bibliographic record

VenueSSRN Electronic Journal · 2001
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsYork University
Fundersnot available
KeywordsKyoto ProtocolCarbon sequestrationIncentiveBiodiversityCarbon sinkNatural resource economicsCarbon offsetEndangered speciesClimate changeBusinessEnvironmental resource managementGeographyEnvironmental protectionAgroforestryEcologyHabitatEconomicsEnvironmental scienceCarbon dioxideBiology
DOInot available

Abstract

fetched live from OpenAlex

The structure of the mechanisms in the Kyoto Protocol with respect to sinks, may be interpreted so as to place incentives on national governments that counter recent progress made towards the preservation of old-growth forests. A focus on the element carbon fails to recognize values other than sequestration that standing forests can provide. For example, an approach that strictly seeks to increase the rate of fixation of atmospheric carbon will favour replacing old-growth forests with mono-cultural plantations of trees. The international community, in implementing these mechanisms, may frustrate other environmental initiatives such as the conservation of endangered species habitat and the protection of biodiversity. Further,focussing on the rate of sequestration is misguided. The recent empirical evidence suggests that the best way to use forests for climate change mitigation is to allow them to grow old, and to protect them from ever being logged.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.007
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.253
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations3
Published2001
Admission routes1
Has abstractyes

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