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Record W2129933944 · doi:10.5558/tfc85885-6

TRIAD zoning in Quebec: Experiences and results after 5 years

2009· article· en· W2129933944 on OpenAlexaffvenueabout
Christian Messier, Rebecca Tittler, Daniel Kneeshaw, Nancy Gélinas, Alain Paquette, Kati Berninger, Héloïse Rheault, Philippe Meek, Nadyre Beaulieu

Bibliographic record

VenueThe Forestry Chronicle · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsResolute Forest Products (Canada)FPInnovationsMinistère des Ressources naturelles et des ForêtsCentre de Géomatique du QuébecUniversité du Québec à Montréal
Fundersnot available
KeywordsZoningTriad (sociology)Forest managementSustainabilityEnvironmental resource managementGeographyEcosystem managementSustainable forest managementForest ecologyEnvironmental planningForestryEcosystemBusinessEcologyPolitical scienceEconomicsSociology

Abstract

fetched live from OpenAlex

The TRIAD approach to forest management involves dividing the forest into 3 zones, each with its own management objectives, but with the overall goal of increasing the ecological and economic sustainability of the forest. For the past 5 years, we have been experimenting with TRIAD zoning in central Quebec, incorporating social interests into the original concept of TRIAD management. Results generally indicate that this approach is economically viable, socially acceptable, and preferable ecologically in this area. Although much remains to be done, thus far the consensus among the various project participants is that this approach may be a good fit for the public forest of Canada. Key words: TRIAD, functional zoning, sustainable forest management, ecosystem management, natural disturbance based management, intensive forestry, mixed plantations, partial cuts, eco-efficiency, conservation, new forestry, green accounting

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.034
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0070.002
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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 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

Citations87
Published2009
Admission routes3
Has abstractyes

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