Bibliographic record
Abstract
Naucni cilj istraživackog problema obuhvata problematiku preduzeća sa stanovištva većeg\nbroja lokacija, organizacionih jedininica, kontrole i procesa unutar njih. Eksperimetalni deo\nistraživanja obavljen je u kompaniji „Omni Surfaces“ lociranoj u Severnoj Americi, tačnije u tri\ngrada: Toronto (Ontario, Kanada), Edmonton (Alberta, Kanada) i Hjuston (Texas, USA).\nZnačaj istraživanja se ogleda se u stalnoj potrebi za iznalaženjem najboljeg rešenja procesa\nupravljanja prostorno dislociranim preduzećima, kako bi se obezbedio maksimalan profit, uz\nistovremeni opstanak i razvoj preduzeća.. Sinhronizacija funkcionisanja procesa sa jedne\nstrane, pravovremeno razumevanje potreba potrošača i prava strategija, sa druge strane, jesu\nključni zahtevi pri razvoju integrisanog modela upravljanja prostorno dislociranim\npreduzećima.\nPredmet doktorske disertacije je istraživanje i razvoj integrisanog modela upravljanja prostorno\ndislociranim preduzećima koji u sebi imati najbolje elemente do sada prepoznate u praksi.\nUpravljanje prostorno dislociranim preduzecima za krajnji cilj ima projektovanje modela koji\nće objediniti sve organizacione jedinice u jednu celinu, koja će sinhronizovanim radom\nodgovarati na potrebe potrošača i praviti profit u granicama očekivanja na svim lokacijama.\nFokusom na potrošača, konkurenciju, profit, fleksibilnost preduzeća i brzi odgovor na potrebe\npotrošača, sa pravim proizvodom i u pravo vreme, model ce moći opstati duži niz godina...
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".