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Increased leaf reflectance in tropical trees under elevated CO<sub>2</sub>

2005· article· en· W2057767680 on OpenAlexaff
Sean C. Thomas

Bibliographic record

VenueGlobal Change Biology · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsUniversity of Toronto
FundersDivision of Environmental BiologyU.S. Department of EnergyNational Science Foundation
KeywordsCanopyEnvironmental scienceReflectivityAtmospheric sciencesChlorophyllEcosystemRadiative forcingPrimary productionTree canopyPhotochemical Reflectance IndexVegetation (pathology)Remote sensingBiologyBotanyChlorophyll fluorescenceEcologyClimate changeGeologyOpticsPhysics

Abstract

fetched live from OpenAlex

Abstract Globally increasing atmospheric CO 2 concentrations are known to affect many aspects of plant physiology and development; however, little attention has been given to leaf and canopy optical properties. Three tropical trees in the Leguminosae, an important canopy tree family in many tropical forests, responded similarly to an experimental doubling of CO 2 partial pressure with a 9–23% increase in spectral leaf reflectance to light in the visible (400–700 nm) waveband. Decreased leaf chlorophyll content under elevated CO 2 may explain part of the observed increase in reflectance. However, analyses that statistically corrected for chlorophyll content effects on reflectance still indicated a significant CO 2 effect. This results, in conjunction with the spectral pattern of the response, suggests that the primary mechanism is increased optical masking of chlorophyll under elevated CO 2 . The magnitude of the increase in leaf reflectance is sufficient to suggest that increased canopy reflectance of tropical forests (and possibly other terrestrial ecosystems) may be an important negative feedback in the response of global net radiative climate forcing to increasing atmospheric CO 2 .

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 score0.855
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.039
GPT teacher head0.271
Teacher spread0.232 · 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

Citations47
Published2005
Admission routes1
Has abstractyes

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