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Record W2092204060 · doi:10.5558/tfc83526-4

Cultures intercalaires avec arbres feuillus : quel potentiel pour le Québec?

2007· article· en· W2092204060 on OpenAlexaffvenueabout
David Rivest, Alain Olivier

Bibliographic record

VenueThe Forestry Chronicle · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgroforestry and silvopastoral systems
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsIntercroppingAgroforestryProfitability indexProductivityEcosystem servicesCompetition (biology)Environmental scienceContext (archaeology)ForestryBusinessEcosystemGeographyEconomicsAgronomyEcology

Abstract

fetched live from OpenAlex

Hardwood plantations constitute a good opportunity to intensify the production of high-quality timber. However, in the absence of short-term investment returns, their economic benefits seem uncertain for landowners. Intercropping systems, which are stimulating interest in many temperate countries, could constitute an innovative way to increase the use of hardwood plantations and maximize their profitability, in particular in the mixedwood plain ecozone in Quebec. Results from recent studies show that intercrops do not usually affect tree growth and could even stimulate it in some cases. Furthermore, in addition to their various environmental benefits related to soil fertility, biodiversity and landscape, intercropping systems are quite often more profitable than conventional forest plantations. Finally, even though trees can compete with crops for light, water and nutriments, decreasing crop productivity, the technical itinerary specific to intercropping systems allows for the control of at least part of this competition. The development perspectives of intercropping systems are discussed, taking into account the biophysical and socio-economic context specific to the mixedwood plain ecozone in Quebec. Key words: environmental benefits, high-value wood, resource competition, tree growth, mixedwood plain ecozone, technical itinerary, hardwood plantation, crop productivity, profitability, intercropping systems

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.957

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 studies0.0010.000
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 head0.014
GPT teacher head0.225
Teacher spread0.211 · 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

Citations10
Published2007
Admission routes3
Has abstractyes

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