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Annual basal area increment and growth duration of <i>Pinus taeda</i> in response to eight years of free‐air carbon dioxide enrichment

2006· article· en· W1979280298 on OpenAlexaff
D. J. Moore, Susanne Aref, Moon‐Ho Ringo Ho, Jeffrey S. Pippen, Jason G. Hamilton, Evan H De Lucia

Bibliographic record

VenueGlobal Change Biology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsMcGill University
FundersU.S. Department of Energy
KeywordsBasal areaCarbon dioxideAnimal scienceEnvironmental scienceVapour Pressure DeficitPinus <genus>ProductivityBasal (medicine)StormAtmospheric sciencesEcologyBiologyBotanyPhotosynthesisMeteorologyGeography

Abstract

fetched live from OpenAlex

Abstract Rising CO 2 is predicted to increase forest productivity, although the duration of the response and how it might be altered by variation in rainfall, temperature and other environmental variables are not well understood. We measured the basal area of rapidly growing Pinus taeda trees exposed to free‐air CO 2 enrichment for 8 years and used these measurements to estimate monthly and annual growth. We used these measurements in a statistical model to estimate the start and end of growth in each year. Elevated CO 2 increased the basal area increment ( BAI ) of trees by 13–27%. In most years, exposure to elevated CO 2 increased the growth rate but not the duration of the active growth period. With the exception of 1 year following an extreme drought and a severe ice storm, BAI was positively correlated with the amount of rainfall during the active growth period. The interannual variation in the relative enhancement of BAI caused by elevated CO 2 was strongly related to temperature and rainfall, and was greatest in years with high vapor pressure deficit. There was no evidence of a systematic reduction in the stimulation of growth during the first 8 years of this experiment, suggesting that the hypothesized limitation of the CO 2 response caused by nitrogen availability has yet to occur.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.995

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 head0.014
GPT teacher head0.217
Teacher spread0.202 · 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

Citations69
Published2006
Admission routes1
Has abstractyes

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