Lizingo (finansinės nuomos) teisinis reglamentavimas Lietuvoje ir tarptautinėje teisėje
Bibliographic record
Abstract
The main purpose of the master thesis is to analyse conception of leasing (financial lease), main elements, problems or legal regulation of leasing in Lithuania, also to highlight the merits and demerits of regulation in comparison with the legal basis of other countries and international documents, as well as to detect gaps in legal regulation. The historic development of leasing is briefly reviewed in the master thesis, the two main types of leasing – financial and operational leasing – are also briefly analysed. Furthermore the conception of leasing (financial lease) in Lithuania is analysed and compared with the conception introduced by European Federation of Leasing Company Associations (Leaseurope) and conception established in international law acts (1988 Ottawa Convention on International Financial Leasing). The substantial features of leasing (financial lease) agreement are distinguished. The legal nature of leasing (financial lease) is also examined in the thesis as well as resemblance to lease, sale-purchase, crediting, loan legal relationships, the main theories are distinguished in the thesis as well as attention paid to the law specialists who favour the theory that leasing (financial lease) is derived from lease and sale-purchase legal relations. In the master thesis attention is drawn to the problem – on the basis of which criterions it is possible to distinguish abovementioned similar legal institutes from leasing (financial lease). The master thesis also... [to full text]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.023 |
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".