Understanding Chemical Pollution at Sea
Bibliographic record
Abstract
Today, the internet has become a key method of communication. Young generations, as well as the wider public, now use smartphones and tablets on a daily basis to find information and gain understanding in different fields. With this as a backdrop, the Center of Documentation, Research and Experimentation on Accidental Water Pollution (Cedre) and Transport Canada naturally decided to pursue their collaboration by developing a website devoted to the issue of chemical pollution at sea. In 2011, a learning guide was released on chemical pollution at sea composed of 2 posters and a booklet. This learning package is intended for 12 to 18-year-olds and their teachers, but also for journalists, those potentially involved in spill response and the general public. This website (www.chemical-pollution.com) is divided into six major sections: an introduction to chemistry and a few examples of its uses; different aspects of shipping, such as the types of ships used and regulations; the main sources of chemical pollution at sea; spill prevention and preparedness; the different response techniques, systematically illustrated with examples of past incidents; the impact on human health, the environment and the economy. This interactive website features a series of original animations. Users can, for instance, discover the organization of a port terminal, view the behavior of different chemicals, and understand techniques used to respond to a spill of bulk cargo. A quiz, with different levels of difficulty, offers users the chance to test their knowledge on this theme. A character named Phosphacola accompanies younger users on a journey to follow a chemical from its extraction to its end use, presenting its transport across the world's oceans and the spill risks to which it may be exposed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".