Aplicación de la metodología Lean Construction para mejorar la productividad en la etapa de estructuras en el Proyecto Ontario II, Chorrillos- Lima, 2018
Bibliographic record
Abstract
La investigación titulada “Aplicación de la metodología Lean Construction para mejorar la productividad en la etapa de estructuras en el Proyecto Ontario II, Chorrillos- Lima, 2018”. \nTuvo como propósito general determinar la influencia al aplicar las herramientas lean \nconstruction en el proceso de construcción del proyecto, con la finalidad de mejorar la \nproductividad en la etapa de estructuras del proyecto en referencia. \nEl diseño del presente estudio cuantitativo de corte transversal-no experimental de \ntipo descriptivo-correlacional y se emplearan encuestas para conseguir la información \nmediante un cuestionario establecido y bien estructurado con un alto grado de validez.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".