UTILIZING RANDOM PERFORMANCE RECORDS IN THE BEST VALUE MODEL / ATSITIKTINE TVARKA PARINKTŲ VEIKLOS EFEKTYVUMO DUOMENŲ NAUDOJIMAS GERIAUSIOS VERTĖS MODELYJE
Bibliographic record
Abstract
Most construction agencies have a quality management system in order to control and manage the quality of their final product. Once the project is over, the testing results are kept in archives in which they are rarely re-visited or utilized. Quality testing results carry much information about the contractor performance that could be useful during the contractor evaluation/selection process. Previous attempts to implement the Best Value (BV) used the average performance records as the expected performance, which was utilized to evaluate contractors. The objective of this research is to develop, based on random data obtained from the contractor's performance records, a methodology that provides decision makers with the level of confidence or risk associated with the contractor selection using the BV model. Simulation technique is used to develop the BV model and analysis. Field performance data have been used to obtain the Percentage Defective, which indicates the contractor's performance in the BV model. The analysis of data indicates that performance follows a normal distribution. Sensitivity analysis of the BV model illustrates the significance of the weights in the BV model, which demands special attention when selecting the parameters’ weights. The developed methodology provides the decision makers with the confidence and risk associated with their selection decision. Santrauka Dauguma statybos imonių galutinio produkto kokybę kontroliuoja ir valdo naudodamos kokybės vadybos sistemą. Projektui pasibaigus, patikrinimo rezultatai laikomi archyvuose ir retai peržiūrimi arba naudojami. Kokybės patikrinimo rezultatuose daug informacijos apie rangovo veiklos efektyvumą, kuri praverstų vertinant (renkantis) rangovą. Anksčiau mėginant diegti geriausios vertės (GV) modelį, veiklos efektyvumas buvo numatomas pagal vidutinius veiklos efektyvumo duomenis ir pagal tai būdavo vertinami rangovai. Šio tyrimo tikslas – pasitelkus atsitiktine tvarka atrinktus duomenis iš įrašų apie rangovo veiklos efektyvumą, sukurti metodiką, kuri sprendimus priimantiems asmenims suteikia pasitikėjimo arba mažina riziką, susijusią su rangovų atranka pagal GV modelį. Kuriant GV modelį ir atliekant analię taikomas imitacijos metodas. Naudojant faktinius veiklos duomenis apie efektyvumą buvo nustatyta procentinė defektų dalis (angl. Percentage Defective), kuri GV modelyje rodo rangovo veiklos efektyvumą. Duomenų analizė rodo, kad veiklos efektyvumas nenukrypsta nuo normaliojo skirstinio. GV modelio jautrumo analizė rodo, kad jame svarbūs reikšmingumai, taigi parametru reikšmingumus reikia rinktis itin atidžiai. Sukurta metodika sprendimus priimantiems asmenims suteikia pasitikėjimo ir mažina riziką, susijusią su pasirinkimo sprendimais.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".