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Record W2027768958 · doi:10.1139/b04-158

Contrasting response of mountain birch to damage by <i>Eriocrania</i> leafminers in polluted and unpolluted habitats

2005· article· en· W2027768958 on OpenAlexvenueno aff
Mikhail V. Kozlov

Bibliographic record

VenueCanadian Journal of Botany · 2005
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsnot available
FundersEuropean Commission
KeywordsShootBetula pubescensBiologyBetulaceaeHerbivoreBotanyHorticultureLarvaAgronomy

Abstract

fetched live from OpenAlex

The leafmining larvae of Eriocrania spp. (Lepidoptera, Eriocraniidae) develop in the expanding leaves of mountain birch, Betula pubescens subsp. czerepanovii (Orlova) Hämet-Ahti. The larva usually consumes over a half of the leaf, implying moderate foliar damage to an individual shoot. I demonstrated that in unpolluted forests, effects of damage are restricted to the mined leaf, which grows smaller and demonstrates higher fluctuating asymmetry than intact leaves. In contrast, in heavily polluted industrial barrens, mining of the single leaf adversely affects the entire shoot; timing and expression of responses depend on shoot type. In infested short shoots, intact leaves grow smaller and more asymmetrical than leaves in control shoots, whereas infested long shoots demonstrated no current-year response; this difference suggests that long shoots are more competitive than short shoots and can acquire additional resources to compensate for herbivore damage. In contrast, in the next year after damage, no consequences of mining were detected in short shoots, whereas infested long shoots produced lower numbers of axillary long shoots than controls. The detected interactive effects of pollution and leaf damage most probably result from resource limitation in birches growing on low-nutritive toxic soils.Key words: Betula pubescens subsp. czerepanovii, foliar damage, fluctuating asymmetry, Kola peninsula, leaf size, shoot growth.

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)0.002
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 score0.839
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.019
GPT teacher head0.266
Teacher spread0.247 · 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

Citations10
Published2005
Admission routes1
Has abstractyes

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