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Record W2977750955 · doi:10.5558/tfc2019-020

Early response of understory vegetation to wood ash fertilization in the sub boreal climatic zone of British Columbia

2019· article· en· W2977750955 on OpenAlexafffundvenueabout
Saskia C. Hart, Hugues B. Massicotte, P. Michael Rutherford, Bruce J. Rogers

Bibliographic record

VenueThe Forestry Chronicle · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsMinistry of ForestsLaurentian UniversityUniversity of Northern British Columbia
FundersUniversity of Northern British ColumbiaNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsUnderstoryWood ashEnvironmental scienceAgronomyVegetation (pathology)NitrogenFraxinusAmendmentNutrientBotanyChemistryEcologyBiologyCanopy

Abstract

fetched live from OpenAlex

Wood ash can be used as a soil amendment in forest ecosystems to alleviate nutrient loss, ameliorate soil acidity, increase tree growth, and reduce landfilled waste. Two hybrid spruce (Picea glauca X engelmannii) plantations in interior British Columbia were treated with two types of bioenergy-produced wood ash (high carbon boiler ash and low carbon gasifier ash) with or without nitrogen fertilizer in a two-way factorial block design. Ash and nitrogen treatments were applied to 8.0 m radius plots at a rate of 5000 kg ha-1 loose ash (dry basis), and 100 kg N ha-1 of urea in pellet form. Changes in understory vegetation cover were observed. There was a significant (p<0.05) effect of nitrogen and wood ash plus nitrogen application on understory vegetation community composition, with nitrogen application having the greatest effect. Discriminant function analysis indicated a differential response of species group to ash/ nitrogen treatments, though the effect size was small. We conclude that short-term changes to understory vegetation are minimal when these two ashes were applied at a rate of 5000 kg ha-1. Continued monitoring will determine if any long-term effects become apparent with time.

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 score0.141
Threshold uncertainty score0.978

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 head0.006
GPT teacher head0.203
Teacher spread0.196 · 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
Published2019
Admission routes4
Has abstractyes

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