Theme Session L - Steering shipping impact prevention towards holistic marine management
Bibliographic record
Abstract
ICES Annual Science Conference Book of abstracts of theme session L: Steering shipping impact prevention towards holistic marine management Conveners: Okko Outinen (Finland), Cathryn Murray (Canada), and Ida-Maja Hassellöv (Sweden) CM 92: Assessing the performance of compliance monitoring devices for the analysis of ballast water samples and under controlled laboratory conditions (2017-2022) CM 151: Trace metals and PAHs discharge from ship with exhaust gas cleaning system (EGCS) CM 180: Examining the effect of new ballast water management regulations for reducing the risk of species introductions to Canadian coastal waters CM 189: Global Environmental Pressures from Shipping CM 219: A decision support model to evaluate the trade-offs in ship biofouling management in the Baltic Sea – North Sea area CM 229: Carbon emissions in UK fisheries: recent trends, current levels, and pathways to Net Zero CM 339: Cumulative effects of contaminants from multiple waste streams, results in an unacceptable risk in areas of intensive shipping activity CM 340: Trophic interactions in marine zooplankton exposed to closed loop scrubber water CM 381: The effect of exposure time and dilution rate of scrubber discharge water on feeding and survival of marine copepods CM 404: Methodological aspects of field observations in ship wakes and shipping lanes CM 488: Global marine biosecurity dynamics and ship lay-ups: intensifying effects of shipping disruptions CM 554: Ecotoxicological effects of closed loop scrubber water on Stickleback and Blue mussel.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.260 | 0.105 |
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".