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Record W1970359279 · doi:10.1139/cjfr-2013-0447

Growth and survival of seven native willow species on highly disturbed coal mine sites in eastern Canada

2014· article· en· W1970359279 on OpenAlexaffvenueabout
Ale× Mosseler, John E. Major, Michel Labrecque

Bibliographic record

VenueCanadian Journal of Forest Research · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsUniversité de MontréalEspace pour la vieNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsWillowBiologyCoppicingLand reclamationBiomass (ecology)Woody plantBotanyEcologyAgronomyHorticulture

Abstract

fetched live from OpenAlex

Significant differences were apparent in seven native North American willow (Salix) species compared and assessed in common garden field tests for survival, biomass production, and coppice structure on former coal mine sites in New Brunswick, Canada. In most species, percentage survival was relatively constant after the initial establishment phase, allowing good prediction of final survival in the first or second year after establishment. Unrooted dormant stem sections collected from clones of five willow species previously field-tested and selected for survival and growth, survived and grew better on the mine site to be reclaimed than those collected directly from natural populations, demonstrating the ability to rapidly improve survival results based on prior field testing. Survival at ages 5 and 6 improved from an average of 70% to 94% for S. eriocephala Michx. and from 42% to 84% for S. interior Rowlee. The best clones in both species had over 95% survival and had approximately 5–6 t·ha −1 (t = tonne) fresh mass after 2 years of coppice growth. We recommend these two species for use in mine reclamation activities, because they grew best overall and had the highest survival rates. Despite poor average rooting ability in S. bebbiana Sarg., S. discolor Muhl., and S. humilis Marshall, some genotypes of these species showed good survival and growth, and further selection for these traits is warranted.

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 score0.300
Threshold uncertainty score0.305

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 head0.041
GPT teacher head0.248
Teacher spread0.208 · 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

Citations47
Published2014
Admission routes3
Has abstractyes

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