T?rk Bo?azlar? ??in Gemi Risk Modeli ?nerisi
Bibliographic record
Abstract
?stanbul ve ?anakkale Bo?azlar?n? kullanan gemilerin risk profilini belirlemeye y?nelik bug?ne kadar herhangi bir model geli?tirilmemi?tir. Gemi ge?i?lerinde al?nan tedbirler "T?rk Bo?azlar? Trafik D?zeni T?z???" esaslar?na g?re gemi boyu ve tehlikeli madde ta??y?p ta??mad??? dikkate al?narak belirlenmektedir. Fakat Avrupa Birli?i'nin Liman Devleti Denetimi Kurumu olan Paris Memorandumu ile Tokyo ve Karadeniz Memorandumlar? ve ABD, Kanada, Avustralya vb. ?lkeler taraf?ndan risk fakt?r? temeline dayanan modeller uygulanarak gemi risk profilleri belirlenmekte ve denetlenecek gemilerin se?imi ile al?nacak ?nlemler bu temeller ?zerine belirlenmektedir. ?zellikle riskli ve ?ok riskli gemiler ?zerinde liman devleti kontrolleri s?kla?t?r?lmakta, ?ok y?ksek risk ihtiva eden baz? gemilerin o ?lke veya memorandum limanlar?na giri?i yasaklanmaktad?r. Bu ?al??mada ?rnek gemi risk modelleri incelenmi?, T?rk Bo?azlar?ndan ge?en gemilere y?nelik yeni bir model olu?turulmu? ve bu gemilerin risk profilleri sunulmu?tur. There has been no study to determine risk categories of the vessels using Turkish Straits so far. Precautions during the passage is determined according to "Traffic Regulations of Turkish Straits" taken into account of ships length and whether her cargo is dangerous or not. However, Paris MoU which is the EU's Port State Control Organization and Tokyo and Black Sea MoU's and countries such as USA, Canada and Australia apply their own Ship Risk Models and selections for inspections are done and precautions are taken accordingly. Especially port state controls are concentrated on high risk and very high risk ships and some of those ships are put on black list and sometimes are banned from entering to their ports. In this study, some examples of ship risk models are evaluated, a new model is proposed for the vessels using Turkish Straits and ship risk profiles presented.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".