On the public citation graph

Using Machine Learning Methods to Predict and Understand Stomatal Behavior Under Different Environmental Conditions

0 signed · 56 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/widm.1379openalex-extracted
Interpretability of machine learning‐based prediction models in healthcare
inferredcites →https://doi.org/10.1007/978-3-642-41136-6_3openalex-extracted
Early History of Support Vector Machines
inferredcites →https://doi.org/10.1007/978-94-009-3535-8openalex-extracted
Progress in Photosynthesis Research
inferredcites →https://doi.org/10.1007/978-94-017-0519-6_48openalex-extracted
A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions
inferredcites →https://doi.org/10.1007/bf00346278openalex-extracted
The role of air humidity and leaf temperature in controlling stomatal resistance of Prunus armeniaca L. under desert conditions
inferredcites →https://doi.org/10.1016/j.agrformet.2015.11.003openalex-extracted
Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods
inferredcites →https://doi.org/10.1016/j.agrformet.2019.03.010openalex-extracted
Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches
inferredcites →https://doi.org/10.1016/j.agrformet.2021.108423openalex-extracted
Machine learning produces higher prediction accuracy than the Jarvis-type model of climatic control on stomatal conductance in a dryland wheat agro-ecosystem
inferredcites →https://doi.org/10.1016/j.jhydrol.2009.08.003openalex-extracted
Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling
inferredcites →https://doi.org/10.1016/j.jhydrol.2012.01.011openalex-extracted
Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios
inferredcites →https://doi.org/10.1016/j.tfp.2021.100139openalex-extracted
Machine learning models perform better than traditional empirical models for stomatal conductance when applied to multiple tree species across different forest biomes
inferredcites →https://doi.org/10.1016/s0921-8181(03)00030-4openalex-extracted
Development of the minimal advanced treatments of surface interaction and runoff
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-extracted
Random Forests
inferredcites →https://doi.org/10.1029/2018ms001500openalex-extracted
Implementing Plant Hydraulics in the Community Land Model, Version 5
inferredcites →https://doi.org/10.1038/nclimate1693openalex-extracted
Temperature as a potent driver of regional forest drought stress and tree mortality
inferredcites →https://doi.org/10.1038/nclimate2550openalex-extracted
Optimal stomatal behaviour around the world
inferredcites →https://doi.org/10.1038/nclimate2641openalex-extracted
Darcy's law predicts widespread forest mortality under climate warming
inferredcites →https://doi.org/10.1038/s41467-021-26107-zopenalex-extracted
From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
inferredcites →https://doi.org/10.1038/s41467-022-29543-7openalex-extracted
A deep learning-based hybrid model of global terrestrial evaporation
inferredcites →https://doi.org/10.1046/j.1365-3040.2002.00926.xopenalex-extracted
A model of stomatal conductance to quantify the relationship between leaf transpiration, microclimate and soil water stress
inferredcites →https://doi.org/10.1071/pp98128openalex-extracted
Temperature dependence of guard cell respiration and stomatal conductance co-segregate in an F 2 population of Pima cotton
inferredcites →https://doi.org/10.1073/pnas.1615144113openalex-extracted
Optimal stomatal behavior with competition for water and risk of hydraulic impairment
inferredcites →https://doi.org/10.1073/pnas.1911694116openalex-extracted
HISTONE DEACETYLASE 9 stimulates auxin-dependent thermomorphogenesis in Arabidopsis thaliana by mediating H2A.Z depletion
inferredcites →https://doi.org/10.1080/01605682.2021.1922098openalex-extracted
Transparency, auditability, and explainability of machine learning models in credit scoring
inferredcites →https://doi.org/10.1080/15592324.2017.1356534openalex-extracted
Stomatal conductance increases with rising temperature
inferredcites →https://doi.org/10.1093/aobpla/plu018openalex-extracted
Poplar saplings exposed to recurring temperature shifts of different amplitude exhibit differences in leaf gas exchange and growth despite equal mean temperature
inferredcites →https://doi.org/10.1093/treephys/2.1-2-3.131openalex-extracted
Stomatal and nonstomatal limitations to net photosynthesis in Pinus taeda L. under different environmental conditions
inferredcites →https://doi.org/10.1093/treephys/24.5.561openalex-extracted
Stomatal sensitivity to vapor pressure deficit and its relationship to hydraulic conductance in Pinus palustris
inferredcites →https://doi.org/10.1093/treephys/27.8.1083openalex-extracted
Inhibition and acclimation of C3 photosynthesis to moderate heat: a perspective from thermally contrasting genotypes of Acer rubrum (red maple)
inferredcites →https://doi.org/10.1098/rstb.1976.0035openalex-extracted
The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field
inferredcites →https://doi.org/10.1098/rstb.2017.0315openalex-extracted
Modelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulics
inferredcites →https://doi.org/10.1104/pp.84.3.658openalex-extracted
The Effect of Temperature on the Occurrence of O2 and CO2 Insensitive Photosynthesis in Field Grown Plants
inferredcites →https://doi.org/10.1109/icdar.1995.598994openalex-extracted
Random decision forests
inferredcites →https://doi.org/10.1111/gcb.13010openalex-extracted
Photosynthesis and isoprene emission from trees along an urban–rural gradient in Texas
inferredcites →https://doi.org/10.1111/j.1365-2486.2010.02375.xopenalex-extracted
Reconciling the optimal and empirical approaches to modelling stomatal conductance
inferredcites →https://doi.org/10.1111/j.1600-0889.2007.00262.xopenalex-extracted
Relationship between temperature and the seasonal course of photosynthesis in Scots pine at northern timberline and in southern boreal zone
inferredcites →https://doi.org/10.1111/nph.16419openalex-extracted
Stomatal optimization based on xylem hydraulics (SOX) improves land surface model simulation of vegetation responses to climate
inferredcites →https://doi.org/10.1111/nph.16485openalex-extracted
Plant responses to rising vapor pressure deficit
inferredcites →https://doi.org/10.1111/nph.16572openalex-extracted
A theoretical and empirical assessment of stomatal optimization modeling
inferredcites →https://doi.org/10.1111/pce.12449openalex-extracted
Temperature responses of mesophyll conductance differ greatly between species
inferredcites →https://doi.org/10.1111/pce.12852openalex-extracted
Predicting stomatal responses to the environment from the optimization of photosynthetic gain and hydraulic cost
inferredcites →https://doi.org/10.1111/pce.14658openalex-extracted
In situ characterisation of whole‐plant stomatal responses to VPD using leaf optical dendrometry
inferredcites →https://doi.org/10.1111/rssb.12377openalex-extracted
Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models
inferredcites →https://doi.org/10.1126/science.1172408openalex-extracted
Regulators of PP2C Phosphatase Activity Function as Abscisic Acid Sensors
inferredcites →https://doi.org/10.1126/science.aaa5931openalex-extracted
Hydrologic connectivity constrains partitioning of global terrestrial water fluxes
inferredcites →https://doi.org/10.1126/scirobotics.aay7120openalex-extracted
XAI—Explainable artificial intelligence
inferredcites →https://doi.org/10.1139/er-2018-0034openalex-extracted
Application of machine-learning methods in forest ecology: recent progress and future challenges
inferredcites →https://doi.org/10.1139/x76-013openalex-extracted
Environmental control of leaf water conductance in conifers
inferredcites →https://doi.org/10.1145/3236009openalex-extracted
A Survey of Methods for Explaining Black Box Models
inferredcites →https://doi.org/10.3390/f12060722openalex-extracted
New Representation of Plant Hydraulics Improves the Estimates of Transpiration in Land Surface Model
inferredcites →https://doi.org/10.48550/arxiv.1612.08468openalex-extracted
Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models
inferredcites →https://doi.org/10.48550/arxiv.1801.01489openalex-extracted
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
inferredcites →https://doi.org/10.48550/arxiv.2009.10513openalex-extracted
Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing
inferredcites →https://doi.org/10.5194/essd-15-5301-2023openalex-extracted
Global Carbon Budget 2023
inferredcites →https://doi.org/10.5194/hess-24-1485-2020openalex-extracted
Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling
inferredcites →https://doi.org/10.60910/7bgu-kqkzopenalex-snapshot
Station for Measuring Ecosystem–Atmosphere Relations (SMEAR II)
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.