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Record W1988666006 · doi:10.5558/tfc86469-4

Stabilité mécanique et caractéristiques racinaires de plants de fortes dimensions de Picea mariana produits en récipients ou à racines nues

2010· article· en· W1988666006 on OpenAlexaffvenueabout
Nelson Thiffault

Bibliographic record

VenueThe Forestry Chronicle · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsMinistère des Ressources naturelles et des ForêtsMinistère des Ressources naturelles et des Forêts (Québec)
Fundersnot available
KeywordsScarificationSeedlingSowingReforestationBiologyForestryRoot systemAgronomyBotanyHorticultureAgroforestryGeographyGermination

Abstract

fetched live from OpenAlex

In Québec (Canada), large planting stock are used for reforestation of high-competition sites, sometimes in combination with soil scarification. Large conifer seedlings are typically produced in containers >300 cm 3 , or as bare-root stock. Nursery practices are expected to influence seedling mechanical stability. We measured large containerized and bare-root black spruce (Picea mariana) seedling stability (resistance to winching), and characterized their root architecture, during their seventh growing season since planting in scarified or non-scarified plots devoid of any competing vegetation. We detected no significant stock type or scarification effect on seedling height, diameter, height/diameter ratio, stability, total number of roots and adventive roots. Occurrences of root deformations, as well as vertical and horizontal root distributions, were not influenced significantly by the treatments. The height/diameter ratio was the sole significant predictor of the resistance to winching. Our results indicate that the use of either large containerized or large bare-root stock has limited silvicultural consequences. In this context, the choice of large stock type should be based on other factors, such as handling constraints.Key words: black spruce, reforestation, stock type, scarification, root system

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 score0.011
Threshold uncertainty score0.999

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 head0.010
GPT teacher head0.261
Teacher spread0.251 · 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

Citations6
Published2010
Admission routes3
Has abstractyes

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