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Record W2163285777 · doi:10.1139/b09-110

Leaf damage has weak effects on growth and fecundity of common ragweed (<i>Ambrosia artemisiifolia</i>)

2010· article· en· W2163285777 on OpenAlexafffundvenue
A. Andrew M. MacDonald, Peter M. Kotanen

Bibliographic record

VenueBotany · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBiological Control of Invasive Species
Canadian institutionsAmorfix (Canada)
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyAmbrosia artemisiifoliaHerbivoreFecundityBotanyInvasive speciesBiological pest controlCompetition (biology)RagweedIntroduced speciesLeaflet (botany)AgronomyEcologyPopulation

Abstract

fetched live from OpenAlex

The Enemy Release Hypothesis predicts that exotic plants gain an advantage over native competitors by losing their natural enemies while invading new regions. However, this assumes that those enemies reduced the performance of these invaders in their native ranges, and this may not be true if an invader is highly tolerant of herbivory. We used a field experiment to test the herbivore tolerance of a North American annual, common ragweed (Ambrosia artemisiifolia L.), which is known to have lost insect herbivores while invading Europe. We clipped leaves to simulate damage by folivores and removed meristems to simulate apical mortality caused by stem borers, and measured the consequences for growth and reproduction. Stem biomass was only reduced by defoliation far in excess of native-range natural damage, while seed production was unaffected by our treatments. Severely damaged plants maintained seed production by allocating relatively more aboveground biomass to reproduction. These results suggest that for the most part, damage by natural enemies may have little impact on this highly tolerant plant; consequently, enemy release may not have provided a significant advantage to this species in Europe. As well, biological control by insect folivores is unlikely to succeed unless it results in very high levels of damage.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.270

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 head0.016
GPT teacher head0.207
Teacher spread0.191 · 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 designBench or experimental
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

Citations15
Published2010
Admission routes3
Has abstractyes

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