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A test of three juvenile plant competitive response strategies

2006· article· en· W4241609408 on OpenAlexaff
Cameron N. Carlyle, Lauchlan H. Fraser

Bibliographic record

VenueJournal of Vegetation Science · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsThompson Rivers UniversityUniversity of British Columbia
Fundersnot available
KeywordsForageBiologyBiomass (ecology)GreenhouseJuvenilePerennial plantVegetation (pathology)Range (aeronautics)SeedlingAgronomyEcologyPlant communityEcological succession

Abstract

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Abstract Questions: 1. Are there competitive response strategies for light in juvenile plants? 2. If so, do plant traits (e.g. seed weight, relative growth rate, height and biomass) correlate with the strategies? Location: Controlled greenhouse study using perennial vegetation typical of wet meadows in Northeast Ohio, USA. Methods: We used two light manipulations in a greenhouse to screen ten replicates of 19 plant species for three proposed competitive response strategies ( ‘escape’, ‘forage’, ‘persist’ ). We measured the time it took a seedling to die and the maximum height achieved when grown in the dark to assess two strategies, persist and escape . The biomass of seedlings when grown under a controlled, low‐intensity, shifting light source was measured to test a third strategy, forage . Results: We found significant variation across species in the measurements used to assess each strategy. The species ranking for each strategy was not concordant across strategies. Traits were found that correlated with the escape strategy (seed weight, height and biomass) and persist strategy (time to reach maximum height). No traits were found that correlate with the forage strategy. Conclusions: There appear to be trade‐offs by plants in the three strategies tested in this study. Species which had the best performance on one strategy typically scored poorly on the other strategies. However, many species fall in the middle range, ranking similarly across the ‘persist’, ‘escape’ , and ‘forage’ strategies.

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)0.002
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 score0.442
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.253
Teacher spread0.242 · 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

Citations11
Published2006
Admission routes1
Has abstractyes

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