7th International Conference on Technologies & Business Models for Circular Economy
Bibliographic record
Abstract
7. mednarodna konferenca o tehnologijah in poslovnih modelih za krožno gospodarstvo: zbornik konference. Fakulteta za kemijo in kemijsko tehnologijo Univerze v Mariboru je v sodelovanju s Strateško razvojno-inovacijskim partnerstvom – Mreže za prehod v krožno gospodarstvo (SRIP – Krožno gospodarstvo), s katerim upravlja Štajerska gospodarska zbornica, organizirala 7. mednarodno znanstvenoraziskovalno, strokovno in razvojno konferenco Tehnologije in poslovni modeli za krožno gospodarstvo (Technologies & Business Models for Circular Economy; TBMCE), ki je potekala od 4. do 6. septembra 2024 v Grand hotelu Bernardin. Teme TBMCE 2024 so zajemale aktualna vprašanja tehnološkega razvoja in odgovornosti družbe na njeni razvojni poti k bolj odgovornemu, krožnemu ravnanju z viri. Konferenčni program je vključeval okroglo mizo z naslovom Prehod v krožno gospodarstvo v Jugovzhodni Evropi, 5 panelnih diskusij, plenarno in 2 uvodni predavanji, ustna predavanja in predstavitve v obliki posterjev. Dogodek je potekal pod pokroviteljstvom Ministrstva za gospodarstvo, turizem in šport in Ministrstva za kohezijo in regionalni razvoj. Kot soorganizatorji so se nam pridružili EIT RawMaterials RIS središča Adria in SPIRIT Slovenija ter Pomurski tehnološki park v sklopu projektov GREENE 4.0 in CI-Hub.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.068 | 0.017 |
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".