Local Names of Fishes in a Fishing Village on the Bank of the Middle Reaches of the Kampar River, Riau, Sumatra Island, Indonesia
Bibliographic record
Abstract
Local ecological knowledge (LEK) originates from people’s experience interacting with ecological systems in their daily lives. LEK therefore encompasses a variety of information on ecological systems and organisms. Knowing the local names of organisms is vital when collecting information from residents and associating a local name with other LEK. The taxonomic name of a biological species follows rules that were developed in the context of conventional natural science, whereas a local name is typically determined by historical and cultural context within a local human community. We aimed to clarify the relationships between local and scientific names of fishes in the middle reaches of the Kampar River, Indonesia. We investigated local names using a questionnaire survey in a fishing village. The villagers spoke a dialect of Malay used in the Kampar River Basin, and the interviewers were born in the area and were able to speak the dialect. We linked 28 local names of fishes to their corresponding scientific names, including three species that may be extirpated species in the local ecological community. More than half of the local names were associated with a scientific name at the genus level or higher. Residents of the settlement closer to the river more often responded with the local names of fishes inhabiting river channels, while those in the settlement farther from the river more frequently responded with the names of fishes that inhabit swamps. Finally, we discuss how information derived from LEK may be useful in ecological conservation even when it is not resolved to the species level.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".