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Record W2094330479 · doi:10.1139/x99-253

Effects of aphids and moth caterpillars on epiphytic microorganisms in canopies of forest trees

2000· article· en· W2094330479 on OpenAlexvenueno aff
Bernhard Stadler, Thomas Müller

Bibliographic record

VenueCanadian Journal of Forest Research · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
FundersBundesministerium für Forschung und Technologie
KeywordsHoneydewBiologyPhyllosphereAphidMicroorganismEpiphyteHerbivoreBeechBotanyPopulationEcologyBacteria

Abstract

fetched live from OpenAlex

Different types of herbivores were investigated for their effects on microorganisms in the phyllosphere of forest trees during the growing season. Aphids on spruce, beech, and oak produced honeydew, which was readily consumed by microorganisms and resulted in two to three orders of magnitude higher densities (colony forming units) of bacteria, yeasts, and filamentous fungi on leaves of infested trees. The amounts of honeydew excreted by different aphid species and their mode of excretion (large droplets, tiny droplets scattered over leaves, production of wax wool) affected the degree to which honeydew could be processed by epiphytic microorganisms. All groups of microorganisms appeared to be energy limited. These results were consistent for different growth media offered to the microorganisms. Leaf-feeding moth caterpillars also positively affected the growth of microorganisms on leaves of beech and oak. The effects were more pronounced for bacteria and yeasts especially on oak. Thus, different functional groups of herbivores positively affected the growth of microorganisms in the phyllosphere of trees. It is suggested that the population dynamics of herbivores and their feeding characteristics are important features, which should be considered when the population dynamics of microorganisms in the canopies of trees is studied.

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 score0.635
Threshold uncertainty score0.964

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.240
Teacher spread0.230 · 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

Citations44
Published2000
Admission routes1
Has abstractyes

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