MétaCan
Menu
Back to cohort
Record W2046912430 · doi:10.1139/x02-029

The utility of the two-pass harvesting system: an analysis using the ecosystem simulation model FORECAST

2002· article· en· W2046912430 on OpenAlexvenueaboutno aff
Clive Welham, Brad Seely, Hamish Kimmins

Bibliographic record

VenueCanadian Journal of Forest Research · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsUnderstoryEnvironmental scienceForestryPopulationSilvicultureEcosystemAgroforestryGeographyEcologyCanopyBiology

Abstract

fetched live from OpenAlex

The ecosystem model FORECAST was used to simulate the yield potential in Saskatchewan mixedwoods of the two-pass harvesting system. The simulated two-pass stand consisted of an overstory population of pure trembling aspen (Populus tremuloides Michx.) with a white spruce (Picea glauca (Moench) Voss) understory. The aspen was removed at year 60, and yields of the understory spruce and resprouting aspen were simulated for 80 years thereafter. The two-pass simulations were compared with two simulated conventional harvesting systems. The first system consisted of a single final harvest at year 140. In the second system, a clearcut was conducted at year 60. White spruce was then planted in the subsequent year at 400, 600, or 800 stems/ha, and aspen also permitted to resprout. Growth was then simulated for a further 80 years. FORECAST projections indicated that the two-pass system might be effective for releasing the white spruce understory, achieving at least a twofold gain in spruce volume relative to conventional methods. Furthermore, total volumes exceeded those derived from the unmanaged stand, while second rotation yields of aspen declined with spruce understory density. These simulations suggest the two-pass harvesting system has strong potential as a tool for mixedwood management.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.128
GPT teacher head0.338
Teacher spread0.210 · 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.

Study designSimulation or modeling
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

Citations30
Published2002
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicForest Management and PolicyFrench-language works237,207