Possible contributions of globalization in creating and addressing sea horse conservation problems
Bibliographic record
Abstract
GLOBAL CONTEXT For economic, ecological, and social reasons, it is important to explore how globalization might be changing conservation and management of fisheries. In international arenas, little attention has been given to the exploitation of species for non-food purposes ranging from medicines to bioremediation to souvenirs. Such fisheries range from very small-scale catches for personal use (e.g., bait: McPhee and Skilleter 2002) to the cumulatively large and valuable (e.g., traditional medicine: Vincent 1996), with catches commonly traded internationally. These fisheries often extract species that are little studied. Unmonitored extraction makes it difficult to deduce the economic, social, or cultural consequences of fishing. In general, fishery resources are being exploited at a rate that clearly endangers sustained access to market and non-market values that people attribute to them. Overfishing has reduced the biomass of many of the world's major marine living resources to only a small fraction of their former levels (e.g., Jackson et al . 2001; Pauly et al . 2002; Myers and Worm 2003). As a result, many marine fish species are now considered threatened (IUCN 2003), with most such analyses dating from 1996. In 2002, the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) decided that exports of some marine fishes of commercial importance should be brought under management for the first time (CITES 2004). In addition to numerical declines, the compositions of fish communities (Pauly et al . 1998) and whole ecosystems (Jackson et al . 2001) have changed dramatically through overexploitation.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".