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Consequences of differing competitive abilities between juvenile and adult plants

2006· article· en· W1985095167 on OpenAlexafffund
Eric G. Lamb, James F. Cahill

Bibliographic record

VenueOikos · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Alberta
FundersUniversity of AlbertaAlberta Conservation Association
KeywordsSeedlingBiologyInterspecific competitionCompetition (biology)JuvenileCompetitor analysisPerennial plantEcologyRange (aeronautics)AgronomyEconomics

Abstract

fetched live from OpenAlex

The competitive ability of perennial plants can change with life‐stage, but whether these changes have fitness consequences is unknown. We present a simple model of two components of fitness, mortality and flowering rates, for two grassland species with very different patterns of competitive ability and life‐stage. Achillea millefolium seedlings are poor competitors while the adults are good competitors. In contrast, Solidago missouriensis seedlings and adults have similar competitive ability. Models of the two species show that the overall effects of competition on growth are more important than interspecific differences in competitive ability in determining mortality and flowering rates, though the higher seedling competitive ability of S. missouriensis relative to A. millefolium seedlings does result in slightly lower mortality and higher flowering rates for the former species. Simulations where both average competitive ability and relative seedling and adult competitive ability are varied predict that dominant species with high overall competitive ability should experience no advantage or disadvantage from varying competitive ability through development. When overall competitive ability is moderate, the relative costs and benefits of differential competitive abilities among adults and seedlings are variable. High seedling competitive ability relative to adult competitive ability should be favored among species with low overall competitive ability. We predict that communities with high intensity of competition should have a high frequency of species with high seedling competitive ability, while communities with lower intensity of competition should have species with a wide range of relative seedling and adult competitive ability.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.217

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 studies0.0000.001
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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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

Citations36
Published2006
Admission routes2
Has abstractyes

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