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Record W3000705182 · doi:10.1111/oik.06996

Synchronous and asynchronous root and shoot phenology in temperate woody seedlings

2020· article· en· W3000705182 on OpenAlexaff
Kobayashi Makoto, Scott D. Wilson, Takao Sato, Gesche Blume‐Werry, Johannes H. C. Cornelissen

Bibliographic record

VenueOikos · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPhenologyEvergreenBiologyShootDeciduousTemperate climateHabitTemperate forestBotanyWoody plantEcology

Abstract

fetched live from OpenAlex

Understanding variation in root and shoot growth phenology among species is crucial to understanding underlying mechanisms of temporal niche differentiation. However, little is known about the relationship between root and shoot phenology, or how this relationship varies among functional traits. We examined fine root and shoot phenology of 42 seedlings representing a variety of woody species that inhabit the cool temperate forests of northern Japan. Some aspects of phenology were common to the pool of species examined: we found positive relationships between root and shoot phenology for the end of growth, and for the duration of growth, but not for the start of growth. Further, seedlings that started root growth relatively early also ended root growth relatively late. Other aspects of phenology varied predictably with functional traits, i.e. leaf habit and successional status: first, root growth in evergreen species started significantly earlier and ended later than in deciduous species; second, early successional species had the longest duration of shoot growth among all successional types. Our results suggest that niche differentiation may be promoted by differences in phenology between root and shoots, likely contributing to the co‐existence of woody seedlings in temperate forests.

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 score0.009
Threshold uncertainty score0.367

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)0.0000.000

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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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

Citations37
Published2020
Admission routes1
Has abstractyes

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