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Record W2049505888 · doi:10.5558/tfc82070-1

Viability of carbon offset-generating afforestation projects in boreal Ontario

2006· article· en· W2049505888 on OpenAlexafffundvenueabout
Jeffrey Biggs, Susanna Laaksonen-Craig

Bibliographic record

VenueThe Forestry Chronicle · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsAfforestationCarbon offsetKyoto ProtocolCarbon sequestrationNatural resource economicsClean Development MechanismEnvironmental scienceBusinessProductivityInvestment (military)Environmental economicsEnvironmental resource managementAgroforestryGreenhouse gasEconomicsEcologyCarbon dioxide

Abstract

fetched live from OpenAlex

Carbon offsets generated under the Kyoto Protocol (KP) should be included in the management options considered by resource managers. This paper investigates investments in afforestation for the generation of KP-compliant carbon offsets in the Timmins Management Unit, concentrating on the availability of quality carbon budget models, domestic carbon market concerns and the presence of an enabling environment. A modelling exercise is undertaken using GORCAMWC1, with ownership, leading species, investment horizon, site productivity and carbon price as variables. Under current institutional frameworks, afforestation projects with the purpose of generating carbon offsets in the TMU are not viable investments for the first commitment period, though such projects will be profitable under certain conditions if constraints are removed and investment is long term. Key words: afforestation, Kyoto Protocol, boreal Ontario, carbon sequestration

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 score0.374
Threshold uncertainty score0.658

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)0.0000.000

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 head0.013
GPT teacher head0.239
Teacher spread0.226 · 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

Citations6
Published2006
Admission routes4
Has abstractyes

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