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Temperatures within Flowers and Stems: Possible Roles in Plant Reproduction in the North

2019· article· en· W2939526066 on OpenAlexaff
Peter G. Kevan, Е А Тихменев, Patrícia Nunes‐Silva

Bibliographic record

VenueBulletin of the North-East Science Center · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHerbaceous plantBiologyTaraxacum officinaleBotanyInflorescenceDandelionThistleAir temperatureDormancyEnvironmental scienceHorticultureGeographyGerminationMeteorology

Abstract

fetched live from OpenAlex

temperature regimes within fl owers and stems show that insolation (solar) warming results in tempera-flowers and stems show that insolation (solar) warming results in temperature excesses above the ambient air. the additional heat is important for development of reproductive organs (sporophylls) and presumably for the growth of stems. We review studies on temperature regimes within flowers and note that there are almost none within stems. temperature excesses above ambient air in flowers and within hollow stems are variable but can range up to about 10 o C. In the Magadan area, our cursory survey suggests that stem hollowness is more common (18:4 species) than in other parts of the world (about 50%). our results from recording temperatures with the hollow stems of four species of herbaceous plants (oats (Avena sativa), dandelion (Taraxacum officinale), thistle (Cirsium setosum) and sneezewort (ptarmica salicifolia) in a direct sunlight in Magadan present temperature excesses from about 1 o C to 7 o C depending on species, location, and other factors. Flowering, seed-set, floral and seed presentation all require growth and development of plants which are generally stimulated by heat. the general effects of weather and temperature are highly influenced by micrometeorology within plant organs by biophysical processes that suggest adaptations for insolational heat capture. our studies, although preliminary, open the door to a poorly studied area of micrometeorology.

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 score0.013
Threshold uncertainty score0.385

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 science0.0010.000
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 head0.008
GPT teacher head0.184
Teacher spread0.177 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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