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Record W2987518449 · doi:10.1139/cjfr-2019-0016

Effect of plant-derived smoke water and potassium nitrate on germination of understory boreal forest plants

2019· article· en· W2987518449 on OpenAlexaffvenue
Dean D. Mackenzie, M. Anne Naeth

Bibliographic record

VenueCanadian Journal of Forest Research · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGerminationSmokeStratification (seeds)NitrateUnderstoryPotassium nitrateTaigaEnvironmental scienceSeedlingAgronomyBotanyChemistryEnvironmental chemistryPotassiumBiologyEcologyDormancyCanopySeed dormancy

Abstract

fetched live from OpenAlex

This experiment assessed the effects of plant-derived smoke water, potassium nitrate (KNO 3 ), and their combined effect on germination of cold-stratified and non-stratified seed from 18 native boreal forest plant species. Seeds were treated with smoke water diluted to 1:20, 0.1% KNO 3 , and smoke water + KNO 3 . Nine species responded positively to smoke water; these responses were dependent on the type of stratification, and three of these species only had a positive response to smoke water + KNO 3 solution. Five species responded positively to KNO 3 and four of those were associated with smoke water + KNO 3 solution. Smoke water induced germination of several species, but only for seeds that had been previously cold-stratified. Vaccinium myrtilloides Michx. had the largest increase in germination using smoke water and the most reduced germination using KNO 3 . The interactions between smoke water, KNO 3 , and stratified seeds are not well understood. The effects and applications of smoke water and KNO 3 (or other nitrogen sources) should be further researched to determine alternative approaches to restoration of disturbed boreal forest ecosystems.

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 score0.181
Threshold uncertainty score0.999

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 head0.029
GPT teacher head0.264
Teacher spread0.235 · 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

Citations10
Published2019
Admission routes2
Has abstractyes

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