Henry Felix Woods and the Black Sea/Bosphorus Entrance Maritime Safety System, Then and Now
Bibliographic record
Abstract
The perils of sailing in the Black Sea are legendary, and nowhere was more dangerous than the waters where the Sea and the Bosphorus meet. Vessels carrying the valuable products of the Black Sea basin to market foundered here, and in the 1860s a commission was established to look at how to reduce these losses. The article revisits its deliberations and the leading role played in establishing a maritime safety system by Navigating Lieutenant Henry Felix Woods, a Royal Navy navigation specialist. Many surviving structures of the system have been located and photographed by members of Hiking Istanbul hiking group. Les risques associés à la navigation dans la mer Noire étaient bien connus, particulièrement au confluent de la mer et du Bosphore. Des navires qui transportaient les précieux produits du bassin de la mer Noire à leur mise en marché ont sombré dans cette région. Dans les années 1860, une commission a été mise sur pied pour examiner des façons de réduire ces pertes. Le présent article revient sur ses délibérations et sur le rôle de premier plan joué dans l’établissement d’un système de sécurité maritime par le lieutenant de navigation Henry Felix Woods, spécialiste de la navigation de la Marine royale. Bon nombre des structures du système qui subsistent encore aujourd’hui ont été trouvées et photographiées par des membres du groupe de randonnée Hiking Istanbul.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".