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Record W4408446409 · doi:10.5194/egusphere-egu25-244

Dry roots? What crop water relations tell us about irrigation management

2025· preprint· en· W4408446409 on OpenAlexaff
Thorsten Knipfer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsGovernment of British Columbia
Fundersnot available
KeywordsIrrigationCrop managementCropIrrigation managementAgronomyEnvironmental scienceWater resource managementAgricultural engineeringAgroforestryBiologyEngineering

Abstract

fetched live from OpenAlex

Crop performance under limited soil water availability depends on a successful coordination of physiological processes at root, stem, and leaf level. This includes efficient stomatal regulation, root modifications and prevention of xylem embolism. In woody crops, drought-induced mortality is predominantly linked to xylem hydraulic failure by gas embolism blocking water transport from roots to leaves – but does this matter in a managed agricultural system? In this presentation, I will show experimental data collected under greenhouse and field conditions on the sequence of physiological and anatomical events in response to progressive drought stress. This includes a demonstration of applications of X-ray computed tomography to study leaf, stem and root responses to water stress in hazelnut and poplar. I will discuss the relevance of data in the context of precision irrigation management.

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 categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.799
Threshold uncertainty score1.000

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 communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.240
Teacher spread0.222 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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