MétaCan
Menu
Back to cohort
Record W2158615063 · doi:10.5558/tfc77501-3

Divided land base and overlapping forest tenure in Alberta, Canada: A simulation study exploring costs of forest policy

2001· article· en· W2158615063 on OpenAlexfundvenueaboutno aff
Steven G. Cumming, Glen W. Armstrong

Bibliographic record

VenueThe Forestry Chronicle · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
FundersAlberta-Pacific Forest Industries
KeywordsMillForest managementBusinessLand tenureCertified woodLand useLoggingForestryNatural resource economicsAgroforestryEnvironmental resource managementAgricultural economicsEnvironmental scienceEconomicsGeographyAgricultureEngineeringCivil engineering

Abstract

fetched live from OpenAlex

The forest planning environment in Alberta is complicated by multiple forms of forest tenure and by an arbitrary division of the forest into separate softwood and hardwood land bases. The area within and surrounding the Alberta-Pacific Forest Industries Inc. Forest Management Agreement (FMA) area exemplifies the problem, with a large number of independent forest products companies operating in the area. We model 17 sawmill operators and the Alberta-Pacific pulp mill trying to simultaneously satisfy their mill feedstock requirements from a forest.We examined the inefficiencies introduced by this tenure system using Tardis, a computer simulation model incorporating access development, timber harvest, and regeneration. We examined two scenarios: one representing the business-as-usual case where the 18 forest products companies are operating independently, and one where the forest is managed by one company that harvests timber and delivers it to each of the mills.The costs of the present tenure arrangements are, we believe, substantial enough to warrant a thorough re-examination of forest policy and tenure arrangements in Alberta, specifically with respect to land base designation and overlapping tenures. Key words: forest tenure, simulation modelling, timber harvest scheduling, policy analysis

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 score0.144
Threshold uncertainty score0.466

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 head0.023
GPT teacher head0.249
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

Citations20
Published2001
Admission routes3
Has abstractyes

Explore more

Same venueThe Forestry ChronicleSame topicForest Management and PolicyFrench-language works237,207