Seventy‐One Important Questions for the Conservation of Marine Biodiversity
Bibliographic record
Abstract
The ocean provides food, economic activity, and cultural value for a large proportion of humanity. Our knowledge of marine ecosystems lags behind that of terrestrial ecosystems, limiting effective protection of marine resources. We describe the outcome of 2 workshops in 2011 and 2012 to establish a list of important questions, which, if answered, would substantially improve our ability to conserve and manage the world's marine resources. Participants included individuals from academia, government, and nongovernment organizations with broad experience across disciplines, marine ecosystems, and countries that vary in levels of development. Contributors from the fields of science, conservation, industry, and government submitted questions to our workshops, which we distilled into a list of priority research questions. Through this process, we identified 71 key questions. We grouped these into 8 subject categories, each pertaining to a broad component of marine conservation: fisheries, climate change, other anthropogenic threats, ecosystems, marine citizenship, policy, societal and cultural considerations, and scientific enterprise. Our questions address many issues that are specific to marine conservation, and will serve as a road map to funders and researchers to develop programs that can greatly benefit marine conservation.
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 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) | 0.000 | 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 teacher head, 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".