MétaCan
Menu
Back to cohort

Herbivory patterns in mature sugar maple: variation with vertical canopy strata and tree ontogeny

2009· article· en· W2017699923 on OpenAlexaff
Sean C. Thomas, AGNIESZKA J. SZTABA, Sandy M. Smith

Bibliographic record

VenueEcological Entomology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCanopyBiologyHerbivoreOntogenyMapleBotanyTree canopySugarGallEcology

Abstract

fetched live from OpenAlex

1. Although leaf morphology and chemistry show profound changes as trees age, the consequences of such changes to herbivory have received little attention, particularly late in the ontogeny of canopy trees. 2. Using a mobile aerial lift for canopy access, patterns of leaf damage were evaluated in canopy‐dominant mature sugar maple ( Acer saccharum Marsh) trees ranging from ∼20 to 70 cm in diameter, corresponding to an age range of ∼40–180 years. 3. Herbivore damage patterns varied in relation to both vertical canopy position (among upper‐, mid‐, and lower‐canopy positions) and with tree size. Damage types attributable to herbivores active on leaf surfaces, including leaf skeletonizers and leaf cutters (both principally Lepidoptera), and leaf stippling inducers (Hemiptera) showed decreases with tree size, and with increasing height in the canopy. In contrast, leaf damage from the most abundant gall‐forming arthropod in the system, the eriophyid mite Vasates aceriscrumena , increased markedly with tree size. 4. The results indicate that herbivory patterns vary with both canopy stratum and with tree size in sugar maple, and that the relative strength of vertical stratification and tree ontogeny effects are similar in magnitude. The predominant patterns are of a decrease in herbivory with increasing height in the canopy and with tree size, but certain galling arthropods exhibit the reverse trends.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.997

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)0.0040.000

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 head0.210
Teacher spread0.204 · 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.

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

Citations35
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueEcological EntomologySame topicForest Insect Ecology and ManagementFrench-language works237,207