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Record W2512016640 · doi:10.1139/cjb-2016-0185

Smoke originating from different plants has various effects on germination and seedling growth of species in Fescue Prairie

2016· article· en· W2512016640 on OpenAlexaffvenue
Lei Ren, Yuguang Bai

Bibliographic record

VenueBotany · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsUniversity of Saskatchewan
FundersChina Scholarship Council
KeywordsGerminationSeedlingBiologyAgronomyStrawForbHaySmokeArtemisiaFestucaDarknessBotanyHorticulturePoaceaeGrasslandChemistry

Abstract

fetched live from OpenAlex

Little is known about how smoke, an important germination cue, influences seed regeneration of species in Fescue Prairies. Whether germination and seedling growth responses vary with smoke produced from different materials is still ambiguous. In this study, seeds of four forbs from a Fescue Prairie were primed in serial dilutions of aqueous smoke solutions produced from alfalfa (Medicago sativa L.), prairie hay (Festuca hallii (Vasey) Piper), and wheat straw (Triticum aestivum L.), and incubated at 10–0 °C or 25–15 °C in a 12 h light – 12 h dark cycle or 24 h darkness for 49 d. Nonprimed seeds and those primed in distilled water were used as controls. Germination and radical length of Conyza canadensis (L.) Cronquist increased after priming in concentrated smoke-solutions derived from alfalfa, but decreased after priming in the same concentrated smoke solutions made from prairie hay and wheat straw at 25–15 °C in 24 h darkness. Smoke substituted for light improved germination of Artemisia ludoviciana Nutt. Our results indicate that the effect of smoke on seed germination and seedling growth was temperature- and light-dependent. It appears that smoke produced from alfalfa had different compounds that, in turn, had different germination and seedling growth responses as compared with the smoke produced from prairie hay and wheat straw.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.112

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.018
GPT teacher head0.216
Teacher spread0.198 · 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

Citations12
Published2016
Admission routes2
Has abstractyes

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