Ship-Iceberg Collision Database: background, operation, contributions & preliminary analysis
Bibliographic record
Abstract
The Ship-Iceberg Collision Database is a collection of nearly six hundred and sixty ship and iceberg collisions in Northern waters. As the fourth consecutive engineering work term student to contribute to this project under the supervision of Brian Hill, Senior Technical Officer and Ice Tank Supervisor, NRC-IOT, this report outlines the contributions made to the project during the past work term, the methods of research utilized, and a preliminary analysis of the findings. It is intended that this project and report provide a means of forecasting the probability of collision with an iceberg in Northern waters, in an effort to engender safer ocean travel and stimulate further research and development regarding factors that affect the Safety of Life at Sea (SOLAS). Numerous research methods and sources were utilized in contributing to this project, primarily contemporary shipping registries, newspapers and gazettes. Trends and correlations discovered amongst incident parameters are examined using linear regression modeling, and prevalent findings are further discussed within the report. The preliminary analysis develops noted relationships between known incidents and collision parameters such as climatic conditions, vessel speed, and sea ice extent and iceberg sightings on the Grand Banks of Newfoundland. Historical fluctuations in sea ice extent, iceberg sightings and corresponding collisions are represented, providing insight into bygone disastrous eras. Though collisions now occur at a rate of approximately one to two incidents per year, the threat posed by ice has not diminished. Modern iceberg detection technologies such as radar, and the introduction of ice and iceberg monitoring agencies such as the International Ice Patrol, have been integral to the decrease of ship and iceberg collisions realized during the past century. Preventative measures must be furthered, and it is recommended that this project be maintained and marketed as a valuable online resource of ship and iceberg collision documentation and a basis for probabilistic risk analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".