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Record W2106422345 · doi:10.5558/tfc83187-2

A stand density management diagram for spruce–balsam fir mixtures in New Brunswick

2007· article· en· W2106422345 on OpenAlexafffundvenueabout
D. Edwin Swift, Margaret Penner, Rolland Gagnon, Jason Knox

Bibliographic record

VenueThe Forestry Chronicle · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsUniversity of New BrunswickNatural Resources CanadaCanadian Forest Service
FundersCanadian Forest ServiceU.S. Forest ServiceNatural Resources CanadaU.S. Department of Agriculture
KeywordsBalsamAbies balsameaBlack spruceForestryThinningNatural regenerationSilvicultureGeographyEnvironmental scienceBotanyHorticultureBiologyTaiga

Abstract

fetched live from OpenAlex

Balsam fir (Abies balsamea (L.) Mill.), red spruce (Picea rubens Sarg.), black spruce (P. mariana (Mill.) BSP), and white spruce (P. glauca (Moench) Voss) often form mixed stands throughout northeastern North America. After harvesting operations or natural disturbances, the resulting natural regeneration may require thinning prescriptions to achieve the desired future stand structure and associated forest products. Stand density management diagrams (SDMDs) can assist the forest manager in examining potential yield implications of stand density management decisions. Data from New Brunswick, Nova Scotia, and Quebec indicate a single SDMD is not appropriate for mixtures of balsam fir and spruce (red and black). The maximum size density line is flatter for mixtures than for pure species stands and the quadratic mean diameter isolines are affected by the species composition. The top height isolines are independent of species composition. The results indicate the SDMD for spruce–balsam fir mixtures needs to be dynamic, incorporating the species proportions. The SDMD has been incorporated into software that prompts the user for the balsam fir fraction and generates the appropriate SDMD. Key words: Acadian Forest Region, eastern species mixtures, thinning decisions

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)0.001
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 score0.424
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.238
Teacher spread0.230 · 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

Citations9
Published2007
Admission routes4
Has abstractyes

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