Historical use of coastal wetlands by small-scale fisheries in the Northern Gulf of California
Bibliographic record
Abstract
Abstract Coastal wetlands are rich and productive ecosystems that historically have been used by small-scale fisheries due to their role as refuges, feeding, and nursery habitats for commercial target species. We used wetland resource users’ Local Ecological Knowledge to document historical patterns of commercial species abundance, areas of fishing importance, trophic level, and species richness and composition in coastal wetlands in the Northern Gulf of California, Mexico. We also reconstructed the environmental history of coastal wetlands in this region from bibliographic sources and photographic records, to document impacts that could have affected coastal fisheries. We found a consistent downward trend in target species abundance; the decrease was perceived as more pronounced by fishers that began fishing in or prior to the 1950’s, pointing to shifting baselines, the failure for resource users to recognize environmental change and accept degraded states as normal. Areas of fishing importance within coastal wetlands also decreased through time. Trophic level of catch showed no distinct pattern across wetland sites or time. Perceived species richness and composition increased with wetland size. Our analysis of the small-scale use of coastal wetlands in the Northern Gulf is relevant to food security and can provide insight into how local populations adapt to depleted coastal food webs.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".