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Record W2072742887 · doi:10.1139/b01-109

Temporal variation in total leaf phenolics concentration of<i>Quercus robur</i>in forested and harvested stands in northwestern Spain

2001· article· en· W2072742887 on OpenAlexvenueno aff
Felisa Covelo, Antonio Gallardo

Bibliographic record

VenueCanadian Journal of Botany · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsnot available
Fundersnot available
KeywordsQuercus roburBiologyBetula pendulaPinus pinasterBotanyIntraspecific competitionHerbivoreScots pineHorticulturePopulationPinus <genus>Ecology

Abstract

fetched live from OpenAlex

Phenolic compounds show intraspecific variation and this may be important in resistance of plants to herbivory. Changes in total leaf phenolics concentration in young Quercus robur L. trees growing under pine canopies and those growing in recently tree harvested areas were studied for 3 years in northwestern Spain. The oaks from the felled areas had a greater leaf phenolics concentration than those under pine canopies and showed less variation between individuals from the population. The average leaf phenolics concentrations also varied significantly between study years. The variations during leaf development and growth are in accordance with the majority of hypotheses that explain investment in secondary metabolism compounds. Leaf phenolics concentrations decreased rapidly during leaf maturity and senescence, but this decrease depended on the time of leaf shedding, the concentration being substantially lower in the year when leaves had been attached longer to the tree. Variation of leaf phenolics concentration was greater in senescent leaves than in green leaves. Such high concentration variability represents a source of spatial and temporal heterogeneity not only for potential herbivores but also for the soil nitrogen cycle in terrestrial ecosystems.Key words: total phenolics, northwestern Spain, Quercus robur, Pinus pinaster, forest harvest, leaf senescence.

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 score0.808
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.235
Teacher spread0.211 · 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

Citations46
Published2001
Admission routes1
Has abstractyes

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