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Record W3109360521 · doi:10.1139/cjb-2020-0129

Root growth phenology, anatomy, and morphology among root orders in <i>Vaccinium macrocarpon</i> Ait.

2020· article· en· W3109360521 on OpenAlexvenueno aff
Amaya Atucha, Beth Ann Workmaster, Jenny Bolivar-Medina

Bibliographic record

VenueBotany · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsnot available
FundersCranberry Institute
KeywordsBiologyPhenologyBotanySecondary growthRoot systemCambiumGrowing seasonVascular cambiumMycorrhizaSymbiosisXylem

Abstract

fetched live from OpenAlex

Understanding the mechanisms controlling plant growth is essential in order to maintain and increase productivity in managed ecosystems. However, the lack of information on below-ground growth compared with above-ground growth limits our ability to adjust crop management practices under changing climate conditions. This study examines seasonal fine-root growth and its spatial distribution through the soil profile across the growing season, and the anatomical and morphological traits of roots according to their branching order in Vaccinium macrocarpon Ait. Root production followed a unimodal curve, with one marked flush of root growth starting at bloom, with a peak at the end of fruit maturation. Root vertical distribution concentrated in the upper 5 cm of soil depth, accounting for over 50% of new roots produced during the study. Root anatomy and morphology were related to root function, as the first three root orders had intact cortex and epidermis and high mycorrhizal colonization, indicative of absorptive function, while orders ranking fifth and higher had secondary development and the presence of a cambium cork layer, indicative of translocation. Our study highlights the importance of examining the timing of root growth and root traits by root order, and its implications for the timing of fertilization and other practices in managed 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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.317

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 communication0.0000.000
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 head0.013
GPT teacher head0.204
Teacher spread0.192 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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