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Record W2943297539 · doi:10.5539/jas.v11n6p105

Biomass of Microalgae via Root Under the Production of Yellow Passionfruit Seedlings

2019· article· en· W2943297539 on OpenAlexvenueno aff
George Alves Dias, Railene Hérica Carlos Rocha Araújo, Wellington Guedes Alves, Ágda Malany Forte de Oliveira, Diogenes Damarsio Andrade de Sousa, José Franciraldo de Lima, Izabela de Moraes Santos, Kalinny de Araújo Alves, Josinaldo Lopes Araújo

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsnot available
Fundersnot available
KeywordsShootDry weightScenedesmusHorticultureBiologySeedlingBiomass (ecology)Randomized block designBotanyRoot systemAgronomyAlgae

Abstract

fetched live from OpenAlex

Seedlings production is one of the most important stages of the production system and directly influences the performance of the plant in the field. In this sense, the present work aims to evaluate the use of biomass doses of Spirulina platensis and Scenedesmus sp. via root system on the production of yellow passion fruit seedlings. A greenhouse experiment was carried out using a randomized block design in a 2 × 5 factorial scheme (Spirulina platensis and Scenedesmus sp., at the doses 0.0%, 0.2%, 0.4%, 0.8 % and 1% m/v), with four blocks and two experimental units per plot. At 60 days, leaf number, total seedling length, shoot diameter, root system length, shoot length, shoot and root fresh mass, dry mass, root dry mass and total chlorophyll were measured. Spirulina platensis showed superior performance compared to Scenedesmus sp., for the variables stem diameter, shoot length, fresh shoot mass, fresh root mass, dry shoot mass and root dry mass. The doses influenced the number of leaves, root fresh mass, root dry mass, shoot length and fresh shoot mass significantly. The best dose of microalga applied was 0.8% in passion fruit seedlings at 60 days.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.219
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2019
Admission routes1
Has abstractyes

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