Eliminación y acopio de basuras de vías públicas que repercuten en la contaminación ambiental y actitud sanitaria de la población de Juliaca, 2021
Bibliographic record
Abstract
The present research work entitled: Elimination and support of the holes in the public highway that \naffect environmental pollution and healthcare activity, the impact of the elimination and support of \nthe holes in the public highway on environmental pollution and activity was determined. care in the \npopulation of juliaca 2021, within the scope of study in four critical areas of juliaca: zone a semester \nof huancané, zone b south east exit to Arequipa, zone c north exit to Cusco and south zone exit to \nPuno. The research design was pre-experimental with a group of cases, a single measurement and \nobservation based on three independent variables, elimination and support of the edges of the public \nroad dependent variable: environmental pollution and intervening variables for a total of 238,000 \ninhabitants for whom the percentage distribution was used 5% of the total population. The sample \nsize was 596 inhabitants, 149 for each study area. The survey/questionnaire instrument technique was \nused for the independent and dependent variables and the health behavior test for the intermittent \nvariable. \nBased on a possible contrast of hypotheses, a multiple regression analysis, analysis of variance and \nchi-square were performed, with a margin of error of 5% and a confidence level of 95%. The writing \nstyle is Vancouver style.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".