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Record W2128142470 · doi:10.1139/x09-201

Assessing the impact of current and projected climates on Douglas-Fir productivity in British Columbia, Canada, using a process-based model (3-PG)

2010· article· en· W2128142470 on OpenAlexafffundvenueabout
Nicholas C. Coops, Robbie A. Hember, Richard H. Waring

Bibliographic record

VenueCanadian Journal of Forest Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space Administration
KeywordsDouglas firSite indexWestern HemlockProductivityIndex (typography)Physical geographyForestryEnvironmental scienceClimate changeCurrent (fluid)GeographyRange (aeronautics)EcologyClimatologyOceanographyBiologyGeology

Abstract

fetched live from OpenAlex

Predicted climate change is expected to significantly affect tree growth in many areas. We used a process-based model (Physiological Principles for Predicting Growth, 3-PG) to evaluate how climatic variation might alter growth of Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco var. glauca (Beissn.) Franco and Pseudotsuga menziesii (Mirb.) Franco var. menziesii ) across biogeoclimatic zones in British Columbia. The results indicate that there will be significant changes in site index (defined as the height (in metres) of dominant trees at 50 years) over this century. In the interior, a reduction in site index is likely, particularly in stands with mid-range values of site index (25–30 m), with many of the interior bioecoclimatic zones predicted to experience a gradual mean decrease in site index by up to 10%. Individual sites may decrease by as much as 40% from current values. In contrast, site index along the coast overall is predicted to increase to a maximum of 43 m by 2080. In the Coastal Western Hemlock zone, however, mean site index is likely to increase from 26 m to only 34 m. We believe that combining process-based models with fine-spatial resolution climate forecasts offers a viable approach to assess future changes in forest productivity.

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 score0.026
Threshold uncertainty score1.000

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 studies0.0000.001
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 head0.050
GPT teacher head0.364
Teacher spread0.314 · 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

Citations62
Published2010
Admission routes4
Has abstractyes

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