An Integrated Approach to Sound Management of Certain "Multipurpose" Chemical and Dual-Use Materials in Croatia
Bibliographic record
Abstract
Croatia is a country experiencing economic and post-war reconstruction. Being a Central-European, Mediterranean and South Eastern European country, Croatia is neighboring the still unstable part of Europe. Its chemical industry is widely diversified, and it is predominantly dependent on the imported raw materials, mostly chemicals. Croatia is also rapidly developing its tourism and transport industry as important economic sectors. To strengthen the security in the widest sense, especially to prevent global crime and terrorism, as well as improve its integration into global economy, Croatia has signed numerous international agreements that regulate the control and handling of certain (multipurpose) goods and chemicals (Chemical Weapons Convention/CWC/, Montreal Convention etc.). In addition Croatia is very active in finalizing the work on entering into several others (Rotterdams /PIC/Convention) or participate very active in preparing, for example, Stockholms /POPs/Convention, Protocol of BTWC. In order to enable it to fulfill its international obligations in a timely and professional manner and adopt an integrated, more efficient and transparent approach to the chemicals, Croatia should revise its national implementing legislation and institutional organization, as current administrative and executive potential as well as financial strength are limited. The paper illustrates existing practices as well as discuss options for future work.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".