Evaluación superficial mediante el método PCI del pavimento flexible, carretera Jaén – San Ignacio, tramo San Lorenzo – Santa Elena, Jaén – 2021
Bibliographic record
Abstract
La presente investigación tuvo como objetivo evaluar el estado superficial mediante la metodología PCI del pavimento flexible de la carretera Jaén - San Ignacio, tramo San Lorenzo – Santa Elena, la metodología de investigación es de tipo cuantitativa y diseño no experimental, la muestra fueron 5km de pavimento, se evaluaron un total de 80 unidades de muestreo, se aplicó el método PCI para determinar la condición superficial del pavimento en estudio. Como resultado se obtuvo un IMD de 1991 vehículos por día, la falla con mayor porcentaje es la conocida como grieta de borde con un 27%, seguida de exudación con un 24% y piel de cocodrilo con un 22%, los daños con nivel de severidad leve representan el 72% y los daños con un nivel de severidad medio representan un 28% y un PCI promedio de 70. Concluyendo que la condición del pavimento flexible es de buena, presenta fallas de severidad media pero en menor cantidad, por lo que se recomienda aplicar otros métodos de evaluación que permitan contrastar o establecer las diferencias con respecto a los resultados obtenidos mediante el método PCI y realizar un mantenimiento rutinario para seguir garantizando la transitabilidad.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".