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Evaluating the Effects of Environmental Stress on Leaf Chlorophyll Content as an Index for Tree Health

2022· article· en· W4220670300 on OpenAlexaff
Fatemeh Talebzadeh, Caterina Valeo

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLeaf Properties and Growth Measurement
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsChlorophyllEnvironmental scienceChlorophyll aBiomass (ecology)CarotenoidPhotosynthesisPollutantPhotosynthetic pigmentChlorophyll bWater contentEnvironmental stressBotanyAgronomyBiologyEcologyEnvironmental protection

Abstract

fetched live from OpenAlex

Abstract Chlorophyll content plays a vital role in photosynthetic and biomass production in all plants. Because chlorophyll shows a greater sensitivity to changes in external conditions than do other pigments in foliage, such as carotenoids for example, chlorophyll content in leaves may be a good surrogate for environmental stress, changes in temperature and humidity, as well as in pollutant levels both in the air and in the soil. This paper reviews the potential for chlorophyll content in the leaves of trees as a measure of tree health, resistance to stress and environmental conditions. Because chlorophyll content is shown to decrease dramatically with increases in pollution, non-destructive methods for evaluating the amount of chlorophyll in leaves and its changes over a time may be a sufficient indicator for environmental pollutant levels in the air, in the soil and in the water used by a tree.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.061
GPT teacher head0.246
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), 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

Citations56
Published2022
Admission routes1
Has abstractyes

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