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Record W3140299880 · doi:10.17076/eb1383

Algal Extracts as Plant Growth Biostimulants

2021· article· en· W3140299880 on OpenAlexfundno aff
Татьяна Геннадиевна Шибаева, Е. Г. Шерудило, А. Ф. Титов, Tatyana Shibaeva

Bibliographic record

VenueProceedings of the Karelian Research Centre of the Russian Academy of Sciences · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Industry and Aquatic Biology
Canadian institutionsnot available
FundersUniversity of TasmaniaEuropean CommissionUniversity of CambridgeYork UniversityU.S. Department of Agriculture
KeywordsCuttingRhizosphereShootGerminationAbiotic stressPlant growthAbiotic componentAgricultureNutrientBiologyAgronomyBotanyBiotechnologyEcologyBacteria

Abstract

fetched live from OpenAlex

The review summarizes and systematizesthe data accumulated in recent years as a result of research and commercial trials of seaweed extracts (SE). The results of the assessment of SE as a special class of biostimulants and the key problems of the manufacturing process of SE biostimulants are presented. The chemical composition of SE and a range of their positive effects on plants, such as accelerating seed germination and rooting of cuttings, stimulating the growth of shoots and roots, increasing the efficiency of nutrient uptake and plant yield are described. Evidence is given for the ability of SE to alleviate plant stress caused by various abiotic or biotic factors. Possible involved mechanisms at metabolic and genetic level are discussed. Examples of the influence of SE on plant rhizosphere are considered. The priority tasks are stated, the solution of which should determine the prospects for the use of SE in agriculture in the coming years.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.082
GPT teacher head0.314
Teacher spread0.231 · 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 designBench or experimental
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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Karelian Research Centre of the Russian Academy of SciencesSame topicFood Industry and Aquatic BiologyFrench-language works237,207