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Record W2897574855 · doi:10.5539/jas.v10n11p249

Substrates and Irrigation Frequencies in the Development of Seedlings of Schizolobium parahyba var. amazonicum

2018· article· en· W2897574855 on OpenAlexvenueno aff
José Darlon Nascimento Alves, Wendel Kaian Oliveira Moreira, Leilane Avila Bezerra, Shirlene Souza Oliveira, Tayssa Menezes Franco, Ricardo Shigueru Okumura, Raimundo Thiago Lima da Silva, Inayara Albuquerque Oliveira, Francisco de Assis do Nascimento Leão

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsnot available
Fundersnot available
KeywordsSeedlingDry matterShootIrrigationHorticultureSubstrate (aquarium)Dry weightManureBiologyAgronomyEcology

Abstract

fetched live from OpenAlex

Knowledge on the ideal conditions for the formation of high quality seedlings is fundamental to guarantee establishment success of crops in a safe and efficient manner. Here, we evaluate the effect of different substrates and irrigation frequencies on the initial growth of parica (Schizolobium parahyba var. amazonicum) seedlings. The experiment was conducted in a greenhouse at the Federal Rural University of Amazônia, Capitão Poço, PA. Several variables were analyzed including seedling height, stem diameter, number of leaflets, shoot dry matter, root dry matter, total dry matter, height and stem diameter ratio, shoot dry matter ratio and root dry matter. We found significant differences in seedling development between the applied treatments, including a significant interaction between substrate type and irrigation regime on seedling height, stem diameter, the number of leaflets and plant growth indices, with the best response for proportions 75% soil + 25% bovine manure and 50% soil + 50% bovine manure. Therefore, the substrates containing organic compounds resulted in a higher quality of the seedlings, while the sand consistently presented the lowest increases in seedling production under the three experimental irrigation frequencies, and thus is not recommended as a substrate for the development of Schizolobium parahyba var. amazonicum.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.165

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.227
Teacher spread0.207 · 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

Citations14
Published2018
Admission routes1
Has abstractyes

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