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Record W2134740608 · doi:10.1139/x10-129

Effects of thermal model and base temperature on estimates of thermal time to bud break in white spruce seedlings

2010· article· en· W2134740608 on OpenAlexaffvenue
Rongzhou Man, Pengxin Lu

Bibliographic record

VenueCanadian Journal of Forest Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsOntario Forest Research Institute
Fundersnot available
KeywordsGrowing degree-dayDegree dayDegree (music)Atmospheric sciencesAnnual growth cycle of grapevinesEnvironmental sciencePredictabilityBorealChilling requirementBlack spruceTaigaClimate changePhenologyThermalHorticultureBotanyForestryBiologyEcologyMathematicsGeographyShootMeteorologyStatisticsPhysicsDormancy

Abstract

fetched live from OpenAlex

To improve the predictability of bud burst and growth of boreal trees under varying climate, the thermal time for bud break in white spruce ( Picea glauca (Moench) Voss) seedlings was evaluated under a range of temperature conditions in controlled environment chambers. Thermal time requirements were calculated as the sum of growing degree days or growing degree hours above base temperatures ranging from –1 to 5 °C. The results indicated that the common modeling approach, which uses a high base temperature of 5 °C and growing degree days, may not be appropriate for future climatic conditions. Estimates of thermal time requirements using a base temperature of 5 °C varied considerably among temperature treatments and thus would reduce the predictability of bud burst under changing climate. In contrast, estimates of thermal time requirements with lower temperatures closer to 1 °C were relatively consistent among treatments. Growing degree hour models were less sensitive to base temperature than degree day models. These results should help in the selection of appropriate base temperatures and thermal time models in quantification of thermal time for bud burst modeling in other boreal trees.

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)0.001
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 score0.297
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.011
GPT teacher head0.249
Teacher spread0.238 · 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

Citations40
Published2010
Admission routes2
Has abstractyes

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