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Record W2911953999 · doi:10.36829/63cts.v3i2.266

Adaptación y rendimiento de plantas autóctonas de Guatemala en un sistema acuapónico

2017· article· es· W2911953999 on OpenAlexaff
Carlos Valdéz-Sandoval, Dennis Guerra-Centeno, Edvin Aquino-Sagastume, Mercedes Dí­az, Ligia Rí­os

Bibliographic record

VenueCiencia Tecnologí­a y Salud · 2017
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicPlant and soil sciences
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsBiologyCucurbita pepoHorticultureCrotalaria junceaAgronomyGreen manure

Abstract

fetched live from OpenAlex

Se determinó la adaptación y rendimiento de ocho variedades de plantas autóctonas de Guatemala en un sistema acuapónico. Se utilizaron siete módulos acuapónicos, con capacidad de 80 plantas y 25 organismos de tilapia nilótica (Oreochromis niloticus). Se incluyeron en el estudio, plantas de apazote (Dysphania ambrosioides), makuy (Solanum nigrescens), amarantos (Amaranthus sp.), bledo (A. cruentus), chipilí­n (Crotalaria longirostrata), chipilí­n montés (C. vitellina) ayote (Cucurbita argyrosperma) y gí¼icoy (C. pepo). El periodo de evaluación fue de 60 dí­as. Se registró la supervivencia (S), tamaño (T), peso (P) y biomasa o rendimiento (B) de las plantas y los peces al inicio y al final del periodo experimental. Se observó adaptación de siete de las ocho especies de plantas autóctonas, a los módulos acuapónicos experimentales. La variedad de planta con mayor crecimiento fue el ayote, seguida del makuy y el gí¼icoy. Se observaron diferencias en la altura (p=0.00001) y el rendimiento (p=0.00001) de las variedades de las plantas. La biomasa de tilapia por tanque fue _ = 730 ± 75.2 g. En tal sentido, es posible cultivar plantas autóctonas de Guatemala en un sistema acuapónico.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0040.001
Research integrity0.0010.001
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 head0.027
GPT teacher head0.265
Teacher spread0.238 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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