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Record W3044552729 · doi:10.3390/microorganisms8071088

Granular Calcite Stimulates Natural Mycorrhization and Growth of White Spruce Seedlings in Peat-Based Substrates in Forest Nursery

2020· article· en· W3044552729 on OpenAlexaff
Mohammed S. Lamhamedi, Mario Renaud, Isabelle Auger, J. André Fortin

Bibliographic record

VenueMicroorganisms · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsUniversité LavalMinistère des Ressources naturelles et des Forêts
Fundersnot available
KeywordsCalcitePeatSeedlingRhizosphereBotanyEnvironmental scienceBiologyAgronomyChemistryMineralogyEcology

Abstract

fetched live from OpenAlex

The acidity of peat-based substrates used in forest nurseries limits seedling mineral nutrition and growth as well as the activity of microorganisms. To our knowledge, no study has yet evaluated the use of granular calcite as a covering material to increase pH, calcium and CO2 concentrations in the rhizosphere and ectomycorrhizal development. The objective is to compare different covering treatments on early colonization of the roots by ectomycorrhizal fungi, as well as the growth and calcium nutrition of white spruce seedlings in the forest nursery. Three treatments were used to cover the plant cavities (Silica (29 g/cavity; control treatment), Calcite (24 g/cavity) and calcite+ (31 g/cavity)) and were distributed randomly inside each of the five complete blocks of the experimental design. The results show that calcite stimulates natural mycorrhization. Seedlings grown with calcite have significant gains for several growth and physiological variables, and that the periphery of their root plugs are more colonized by the extramatrical phase of ectomycorrhizal fungi, thus improving root-plug cohesion. The authors discuss the operational scope of the results in relation to the tolerance of seedlings to environmental stress and the improvement of their quality, both in the nursery and in reforestation sites.

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 score0.278
Threshold uncertainty score0.320

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 head0.007
GPT teacher head0.187
Teacher spread0.179 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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