Who is the expert? Evaluating local ecological knowledge for assessing wildlife presence in the Peruvian Amazon
Bibliographic record
Abstract
Abstract Amazonian wildlife population studies often employ conventional biological survey methods to assess species presence but a growing number of studies are making use of local ecological knowledge (LEK) household surveys. Despite concerns in the scientific community over the accuracy and precision of LEK, scant research to date compares data from conventional biological survey methods with data gathered from household surveys. An important question among scientists is who should be approached when collecting household survey data for wildlife inventories, both for accurate data and for ethical concerns. In this article, we report on the accuracy and precision of LEK household surveys for wildlife inventories based on data collected along the Napo River in Peru using 10 land transect surveys, 5 river transect surveys, 487 camera trap days, and 37 LEK household surveys. Our findings indicate that the household surveys for wildlife inventories are accurate in comparison to biological survey methods, and that they may outperform conventional surveys for certain aquatic, ephemeral, and cryptic terrestrial species. We find no statistical differences among households defined by participation in hunting, community leadership, or primary livelihood strategy. Community members recommend that scientists first solicit wildlife data from community leaders and then hunters. Through the use of retrospection, LEK surveys can reveal how wildlife presence changes through time in remote, data‐poor tropical forests. Use of LEK household surveys is recommended as a useful complement to other methods, with attention to collecting data ethically in collaboration with local communities.
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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 | 0.014 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".