What is behind fleet evolution: a framework for flow analysis and application to the French Atlantic fleet
Bibliographic record
Abstract
Abstract Quillérou, E. and Guyader, O. 2012. What is behind fleet evolution: a framework for flow analysis and application to the French Atlantic fleet – ICES Journal of Marine Science, 69: 1069–1077. The study of fishery dynamics considers national-level fleet evolution. It has, however, failed to consider the flows behind fleet evolution as well as the impact of the dynamics of owners of invested capital on fleet evolution. This paper establishes a general conceptual framework which identifies different vessel and owner flows behind fleet evolution and some relationships between these flows. This descriptive conceptual framework aims to change the current focus on drivers of fleet evolution to drivers of the flows behind fleet evolution. We identify a direct impact of vessel flows on the fleet size and nature, and an indirect impact of the movements of capital owners on the fleet evolution (size and nature). This conceptual framework is illustrated using French Atlantic fleet data over a 15-year period (1994–2008). It is shown that the identified flows vary in size and nature and therefore impact differently on the fleet evolution. This description also shows some dependence of vessel flows on owner dynamics. This relationship should be better taken into account for more effective capacity management.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".