Habitat management and restoration as missing pieces in flats ecosystems conservation and the fishes and fisheries that they support
Bibliographic record
Abstract
ABSTRACT Flats ecosystems are dynamic, shallow, nearshore marine environments that are interconnected and provide immense ecological and socio-economic benefits. These habitats support a diversity of fish populations and various fisheries, yet they are increasingly threatened by anthropogenic stressors, including overfishing, habitat degradation, coastal development, and the cascading effects of climate change. Effective habitat management and restoration are essential but are often missing for flats ecosystems. Despite navigating a landscape of imperfect knowledge for these systems, decisive action and implementation of habitat protection and restoration is currently needed through policy and practice. We present a comprehensive set of 10 strategic guiding principles necessary for integrating habitat management and restoration for the conservation of interconnected flat ecosystems. These principles include calls for comprehensive ecosystem-based management, integrating adaptive strategies that leverage diverse partnerships, scientific research, legislative initiatives, and local and traditional ecological knowledge. Drawing on successes in other environmental management realms, we emphasize the importance of evidence-informed approaches to address the complexities and uncertainties of flats ecosystems. These guiding principles aim to advance flats habitat management and restoration, promoting ecological integrity and strengthening the socio-economic resilience of these important marine environments.
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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".