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Record W4407136829 · doi:10.18188/sap.v21i2.29470

Pyroligneous extract for production of Pinus taeda L. seedlings

2022· article· en· W4407136829 on OpenAlexaff
Natalia Maria Martinazzo Angelo, Guilherme Gava Gaboardi, Renan Acácio Almeida, Lucas Smaha Grando, Sonia Purin da Cruz

Bibliographic record

VenueScientia agrária paranaensis/Revista scientia agrária paranaensis · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsPinus <genus>Production (economics)Loblolly pineHorticultureBotanyBiologyEconomics

Abstract

fetched live from OpenAlex

Production of well-developed Pinus taeda L. seedlings is essential for satisfactory development of planted forests. Biostimulant compounds, such as pyroligneous extract, have shown great potential to improve plant growth of some agronomic crops, and therefore should be tested in forestry species as well. Hence, the goal of this study was to evaluate the effects of pyroligneous extract on P. taeda seedlings under nursery conditions. The experiment was carried out under completely randomized conditions, studying three concentrations of extract added to the substrate. Plant height and diameter were measured monthly, and data regarding plant biomass and development were collected after six months. Pyroligneous extract had no effect on seed germination. Plant height was improved in 12.5% only at 30 days after sowing with 2.5% extract added to the substrate. Fresh mass or volume of roots, as well as shoot mass, were not affected by pyroligneous extract. However, root dry mass was increased from 0.258g to 0.335g with 1.0% pyroligneous extract. Root production by Pinus taeda seedlings is significantly improved by adding 1.0% pyroligneous extract in the substrate before sowing. Therefore, addition of 1.0% pyroligneous extract to substrate is recommended to improve root development of Pinus taeda seedlings at nursery.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.019
GPT teacher head0.239
Teacher spread0.219 · 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.

Study designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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