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Record W2075064096 · doi:10.1139/x10-050

Bud break and spring frost hardiness in Picea abies seedlings in response to photoperiod and temperature treatments

2010· article· en· W2075064096 on OpenAlexvenueno aff
Inger Sundheim Fløistad, Aksel Granhus

Bibliographic record

VenueCanadian Journal of Forest Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
Fundersnot available
KeywordsHardiness (plants)Frost (temperature)Picea abiesphotoperiodismAnnual growth cycle of grapevinesHorticultureBiologySeedlingBotanyShootCultivarMeteorologyGeography

Abstract

fetched live from OpenAlex

Spring frost may result in detrimental damage in newly planted Picea abies (L.) Karst. seedlings if their growth rhythm is not sufficiently adapted to the climatic conditions on the forest site. The aims of this study were to evaluate how bud break and spring frost hardiness were influenced by short-day (SD) treatments with different timing and different temperature regimes during bud formation. Following winter storage, frost hardiness was tested after 1, 3 and 5 weeks in forcing conditions. The SD treatment advanced bud break compared with the control seedlings. In comparison, the effects of timing and the different temperatures on bud break were small. The SD treatment improved frost hardiness in first-year needles during dehardening. The early SD treatment resulted in improved frost hardiness in first-year needles and greater root collar diameter compared with later SD treatment. To avoid a second bud flush, it is important that a critical night length is attained when the SD treatment terminates. Low temperatures following the SD treatment resulted in increased hardiness of the needles and decreased hardiness of the stems. The contrasting effect of temperature in different plant tissues demonstrates the importance of examining different tissues following freezing tests.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.694
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.015
GPT teacher head0.274
Teacher spread0.258 · 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

Citations17
Published2010
Admission routes1
Has abstractyes

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