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Record W4379387948 · doi:10.1080/02626667.2023.2218552

Quantifying irrigation uptake in olive trees: a proof-of-concept approach combining isotope tracing and Hydrus-1D

2023· article· en· W4379387948 on OpenAlex
Paolo Nasta, Diego Todini‐Zicavo, Giulia Zuecco, Chiara Marchina, Daniele Penna, Jeffrey J. McDonnell, Anam Amin, Carolina Allocca, Fabio Marzaioli, Luisa Stellato, Marco Borga, Nunzio Romano

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHydrological Sciences Journal · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
Fundersnot available
KeywordsEnvironmental scienceTranspirationIrrigationSoil waterXylemTracingTRACERHydrology (agriculture)Water useSoil scienceOlive treesAgronomyGeologyChemistryBotanyComputer science

Abstract

fetched live from OpenAlex

An isotope-enabled module of Hydrus-1D was applied to a potted olive tree to trace water parcels originating from 26 irrigation events in a glasshouse experiment. The soil hydraulic parameters were optimized via inverse modelling by minimizing the discrepancies between observed and simulated soil water content and soil water isotope (18O) values at three soil depths. The model’s performance was validated with observed sap flow z-scores and xylem water 18O. We quantified the source and transit time of irrigation water by analysing the mass breakthrough curves derived from a virtual tracer injection experiment. On average, 26% of irrigation water was removed by plant transpiration with a mean transit time of 94 hours. Our proof of concept work suggests that transit time may represent a functional indicator for the uptake of irrigation water in agricultural ecosystems.

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.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.051
GPT teacher head0.266
Teacher spread0.215 · 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