Contribution of local fishermen to improving knowledge of the marine ecosystem and resources in the Republic of Guinea, West Africa
Bibliographic record
Abstract
We assessed the quality of fishermen’s local ecological knowledge, or LEK, as a potential source of information about coastal ecosystem functioning in the Republic of Guinea. Interviews were conducted by means of partial immersion or repeated surveys at six landing sites. In each site and for each topic, discussions were conducted with 3 to 15 individual fishermen and 1 to 10 groups of fishermen. Knowledge was obtained about habitats, substrate preferences, the location of nurseries, reproductive cycles, fish diet, and the trophic network of the Sciaenid community, the major resource for fisheries in this area. We systematically compared the reliability of the information collected with that of scientific information collected in parallel surveys or published data. The contribution of LEK should be considered on a case-by-case basis. Indeed, LEK could be used as (i) a supplementary source of scientific studies (seabed description), (ii) a basis for new scientific investigation (species reproductive cycle), (iii) the only possibility to obtain information (nursery location), (iv) a surrogate to scientific surveys providing an identical level of validity (fish diets) or a satisfactory proxy (trophic network) in a context of limited resources and data in which wide-ranging knowledge relating to the entire coast must be obtained.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".