Mathematical Bio‐Economics 2.0 for Sustainable Fisheries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Reconciling food security, economic development, and biodiversity conservation in the face of global changes is a major challenge. The sustainable uses of marine biodiversity in the context of climate change, invasive species, water pollution, and demographic growth is an example of this bio‐economic challenge. There is a need for quantitative methods, models, scenarios, and indicators to support policies addressing this issue. Although bio‐economic models for marine resources date back to the 1950s and are still used in fisheries management and policy design, they need major improvements, extensions, and breakthroughs. This paper proposes to design a Mathematical Bio‐Economics 2.0 (MBE2) for Sustainable Fisheries to advance the development of bio‐economic models and scenarios for the management of fisheries and marine ecosystems confronted with unprecedented global change. These models and scenarios should make both ecological and socioeconomic sense while being well‐posed mathematically and numerically. To achieve this, we propose to base the MBE2 framework for Sustainable Fisheries on four research axes regarding the mathematics and modeling of: (i) ecosystem‐based fisheries management; (ii) criteria of sustainability; (iii) criteria of resilience; and (iv) governance and strategic interactions. The associated methodology of MBE2 draws mainly on dynamic systems theory, optimal and viable controls of systems, game theory, and stochastic approaches. Our analysis, which is based on these four axes, allows us to identify the main methodological gaps to fill compared to current models for fisheries management.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it