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Record W1972092603 · doi:10.1155/2012/617392

Large Planting Stock Type and Mechanical Release Effects on the Establishment Success of<i>Picea glauca</i>Plantations in Quebec, Canada

2012· article· en· W1972092603 on OpenAlexaffabout
Nelson Thiffault, Benoît Lafleur, Vincent Roy, Josianne DeBlois

Bibliographic record

VenueInternational Journal of Forestry Research · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsCanadian Forest ServiceUniversité du Québec à MontréalUniversité LavalNatural Resources CanadaMinistère des Ressources naturelles et des Forêts (Québec)
Fundersnot available
KeywordsSeedlingReforestationStock (firearms)SowingHorticultureCompetition (biology)ForestryAgroforestryEnvironmental scienceAgronomyBiologyGeographyEcology

Abstract

fetched live from OpenAlex

We established four experimental plantations to evaluate the main and interaction effects of timing (year of application) of mechanical release and stock type (containerized or bareroot) on the establishment success of large Picea glauca seedlings eight years after outplanting on high-competition sites in Quebec (Canada). We also monitored percent cover and height of target vegetation groups as well as the amount of photosynthetically active radiation available to crop trees. Our results indicate that the use of large containerized or bareroot stock had limited effects on seedling growth, although bareroot seedlings presented higher mortality than containerized seedlings. Mechanical release enhanced seedling growth, compared to the control. Delaying treatment by two years impacted crop tree dimensions, although differences were minimal. Survival was not affected. We conclude that large containerized seedlings should be favoured over bareroot stock for reforestation on high-competition sites and that the release guidelines developed for standard size seedlings are applicable to the large dimension P. glauca stock tested in this study.

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)0.001
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 score0.159
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.031
GPT teacher head0.323
Teacher spread0.292 · 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

Citations16
Published2012
Admission routes2
Has abstractyes

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