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Record W1995965671 · doi:10.1139/b04-014

Survival strategies of <i>Silene tatarica</i> (Caryophyllaceae) in riparian and ruderal habitats

2004· article· en· W1995965671 on OpenAlexvenueno aff
Anne Jäkäläniemi, Anneli Kauppi, A. Pramila, Kalle Vähätaini

Bibliographic record

VenueCanadian Journal of Botany · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to water stress
Canadian institutionsnot available
FundersOulun Yliopisto
KeywordsBiologyRuderal speciesRiparian zoneBotanyEcologyBiological dispersalXylemHabitatSilenePopulation

Abstract

fetched live from OpenAlex

Certain morphological and anatomical structures of riparian plants might be important for their survival during the season when they are exposed to severe stress caused by flooding, burial, fluctuating temperatures, and drought. These rare characteristics were studied as related to their ecological consequences in a threatened plant, Silene tatarica (L.) Pers., in riparian and ruderal habitats. The main differences between the habitats were morphological and closely related to the habitat properties, whereas the basic anatomy of structures was similar. After sand burial, most riparian plants formed vertical rhizomes and new meristems by bud ramification in the stem base near the ground surface. Special anatomical structures of fleshy underground stems and roots seem to allow plants to be resilient. Moreover, some primitive structures, such as vascular elements with helical and scalariform thickenings, collenchyma, and abundant xylem parenchyma, may increase the resilience of organs. High amounts of saccharose in fleshy rhizomes and roots can increase the cold resistance of plants. The flat shape and structures of tiny seeds may enhance the long-distance dispersal by water. We suggest that the synergism of these structures enables the survival of S. tatarica in highly disturbed habitats with fluctuating water levels.Key words: bud clusters, burial, flooding, growth habit, tensile strength, vertical rhizome.

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 score0.598
Threshold uncertainty score0.970

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 head0.012
GPT teacher head0.192
Teacher spread0.180 · 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

Citations14
Published2004
Admission routes1
Has abstractyes

Explore more

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