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Record W3047542293 · doi:10.1139/cjfr-2019-0399

Effect of biochar amendment on the properties of growing media and growth of containerized Norway spruce, Scots pine, and silver birch seedlings

2020· article· en· W3047542293 on OpenAlexvenueno aff
Egle Köster, Jukka Pumpanen, Marjo Palviainen, Xuan Zhou, Kajar Köster

Bibliographic record

VenueCanadian Journal of Forest Research · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
FundersState Forest Management Centre
KeywordsBiocharScots pineAmendmentPicea abiesBetula pendulaAgronomyBiomass (ecology)SeedlingEnvironmental scienceGrowing seasonChemistryBotanyHorticultureBiologyPinus <genus>Pyrolysis

Abstract

fetched live from OpenAlex

Common practices and several studies have demonstrated the positive effect of biochar amendment on climate change mitigation, soil properties, and plant growth. We performed a greenhouse experiment to assess the potential of wood biochar to improve the properties of the growing media and the growth of seedlings in boreal tree species. We added willow biochar (0%, 5%, 10%, and 20%) to raw peat and measured the growth of Norway spruce (Picea abies (L.) H. Karst.), Scots pine (Pinus sylvestris L.), and silver birch (Betula pendula Roth) seedlings. In addition, the co-effect of biochar amendment with 0%, 50%, and 100% fertilization was estimated. We found that using up to 10% of biochar did not reduce the water retention capacity of the growing media significantly. Moreover, biochar amendment significantly increased carbon, nitrogen, potassium, and phosphorus concentrations and had a significant liming effect on the growing media. The biochar amendment increased the aboveground growth of spruce seedlings and root biomass, as well as the root collar diameter, of birch seedlings. Biochar amendment did not affect the quality of seedlings, estimated by the Dickson’s quality index, for spruce and pine, while the quality of birch increased. Based on our results, biochar has potential in forest seedling production.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.036
GPT teacher head0.252
Teacher spread0.216 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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