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Record W4311193838 · doi:10.1139/cjfr-2022-0223

Does ontogeny matter for the spring temperature requirement for bud burst of two coniferous species in cool temperate forests?

2022· article· en· W4311193838 on OpenAlexvenueno aff
Kobayashi Makoto

Bibliographic record

VenueCanadian Journal of Forest Research · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
FundersInamori Foundation
KeywordsPhenologyLarchBiologyAnnual growth cycle of grapevinesLarix kaempferiGrowing seasonTemperate forestTemperate rainforestIntraspecific competitionTemperate climateBotanyEcologyShootEcosystem

Abstract

fetched live from OpenAlex

Spring leaf phenology is an important event for trees to determine carbon fixation during the growing season. However, less is understood about the intraspecific variation in spring leaf phenology and its relationship with the spring temperature requirements of conifers, which is problematic for accurately predicting the influence of spring climate warming on conifers. I monitored bud burst timing and the degree days required for bud burst for seedlings and large individuals of Abies sachalinensis (fir) and Larix kaempferi (larch) over two seasons in northern Japan. Contrary to my expectation, the degree days required for the bud burst of small individuals were similar to or larger than those of the large individuals for fir and larch. Consequently, the bud burst timing of small individuals was similar to or later than that of large individuals for fir and larch. Even when conifer species are in their early stage, the spring temperature requirement for bud burst is not necessarily less than that for large individuals, which is not the case for many broad-leaved species. These results indicate that for these two coniferous species, ontogenetic differences in temperature requirements are not necessary to be considered for the response of communities to spring climate change.

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)0.003
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 score0.832
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.307
Teacher spread0.253 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

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