Feeding of <i>Arapaima</i> sp.: integrating stomach contents and local ecological knowledge
Bibliographic record
Abstract
The giant arapaima (Arapaima sp.) has been described as a fish of change in Amazonia because of its important role in the conservation of floodplains, food security and income generation for rural communities. Nonetheless, despite the cultural, ecological and economic importance of arapaima, data on diet are scarce. Aiming to expand knowledge about arapaima diet in western Amazonia, scientific knowledge was integrated with the knowledge of local dwellers. During the low-water period (September 2018) and the falling-water period (June 2019), arapaima stomachs were collected from 11 floodplain lakes in the middle Juruá River. All fishes were measured [TL (total length)] and sexed. Food items from each stomach were categorized as fishes, invertebrates, plants and bone remains and weighed. Also, in the latter period, experienced local fishers were interviewed about arapaima feeding. This integrated approach revealed that young arapaima eat fish and invertebrates but adult arapaima eat fish of a wide range of species, which were mainly of low and intermediate trophic positions. This study reports the first case of cannibalism for arapaima and also shows that during the low-water period, many individuals had empty stomachs or only some small fish-bone remains and/or plant material. Arapaima sex and TL had no influence on the absence of prey in stomach contents. Overall, it can be concluded that local people had consistent ethnobiological knowledge of arapaima feeding ecology that could be useful within management projects in the region.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".