MétaCan
Menu
Back to cohort
Record W2026516273 · doi:10.1139/b08-018

Leaf habit, phenology, and longevity of 11 forest understory plant species in Algonquin State Forest, northwest Connecticut, USA

2008· article· en· W2026516273 on OpenAlexvenueno aff
Jack T. Tessier

Bibliographic record

VenueBotany · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsUnderstoryBiologyLongevityEvergreenDeciduousPhenologyHabitEcologyHabitatShade toleranceCanopy

Abstract

fetched live from OpenAlex

Many functional attributes of plant species are predicated on their leaf habit. To fully understand the way that plant species coexist and respond to future conditions, it is important to have a thorough understanding of the leaf habit, phenology, and longevity of common forest plant species. I quantified these traits in 11 forest understory species in the Algonquin State Forest of northwestern Connecticut, USA, by labeling and monitoring individual leaves of three replicates of each species over a period of 3 years. While clear patterns exist within the evergreen, wintergreen, seasonalgreen, deciduous, and spring-ephemeral groupings, significant differences exist within and among these groups, including differences in the timing of leafing and senescence, and minimum leaf longevity. Because the impact of local and global disturbance is often predicated on the phenological and life-history traits of species, these differences may be important to the responses that these species have to future disturbance. The size of leaf-supporting structures was positively correlated with leaf longevity across species, supporting a predictive connection between construction costs and leaf longevity. Additionally, the leaf habit of Oxalis acetosella L. at this study site is significantly different from that observed previously in the Catskill Mountains, New York State, USA. These differences may be due to local habitat conditions or genetic predisposition.

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 score0.496
Threshold uncertainty score0.512

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 studies0.0000.001
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 head0.211
Teacher spread0.192 · 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

Citations22
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueBotanySame topicEcology and Vegetation Dynamics StudiesFrench-language works237,207