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Record W4318775905 · doi:10.3390/stresses3010019

Field Investigation into Tree Fates from Recent Apple Tree Decline: Abrupt Hydraulic Failure Versus Gradual Hydraulic Loss

2023· article· en· W4318775905 on OpenAlexafffundabout
Hao Xu, Kirsten Hannam, Jesse L. MacDonald, Danielle Ediger

Bibliographic record

VenueStresses · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsXylemRootstockGrowing seasonHorticultureBiologyAgronomy

Abstract

fetched live from OpenAlex

In the last decade, a sporadic tree health syndrome affecting high-density apple plantings in North America has become known as Rapid Apple Decline (RAD) or Sudden Apple Decline (SAD). The affected apple trees were typically grafted on small dwarfing rootstocks, often displayed necrosis at the graft union, and suffered from sudden mortality that occurred over 2–3 weeks amid the growing season or a gradual decline. In 2019 and 2020, we conducted a multi-site investigation in the south Okanagan, British Columbia, Canada, to assess the stem hydraulic characteristics, stomatal conductance, leaf δ13C‰, and fruit dry matter accumulation of the declining trees during disease progression. In trees that died, mortality appeared to be associated with severe disruption in xylem water transport at the damaged graft union, followed by abrupt hydraulic failure. In contrast, symptomatic trees that did not die exhibited the moderately declined plant water relations and a reduction in fruit dry matter accumulation followed by either further deterioration or eventual recovery. This pattern indicates the risk of carbohydrate depletion over gradual hydraulic decline and the importance of timely horticultural remedies. In the present study, we discuss potential horticultural practices to mitigate hydraulic dysfunctions and enhance crop tolerance.

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 score0.594
Threshold uncertainty score0.940

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 head0.260
Teacher spread0.221 · 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

Citations5
Published2023
Admission routes3
Has abstractyes

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