Habitat use of cryptic Green Anaconda ( <i>Eunectes murinus</i> ) in the western Amazon
Bibliographic record
Abstract
Knowledge of Eunectes murinus autecology in the humid, lowland Amazon rainforest of Peru to date is limited. We aggregated and reviewed habitat records from several collaborating projects in the Madre de Dios region of Peru. The records originated from opportunistic sampling, intensive surveying, and radio telemetry tracking. We include details on detection, capture, and processing methodology. Our efforts yielded 180 observations of 50 individual E. murinus, four of which were tagged with very high-frequency radio tags and tracked. We report the first presence of E. murinus for several river tributaries and field sites, and provide insight into the species’ habitat use. In the western Amazon, E. murinus is cryptic and typically exposed only when basking. Of all radiolocations of tagged individuals, in only 17.7% (n = 23) were individuals observable without disturbance. Well-vegetated embankments were used as basking habitats in aguajales, oxbow lakes, streams, and rivers. On rivers, log jams appeared to be the preferred basking habitat. In oxbow lakes and lakes of aguajales, E. murinus basked on fixed and freefloating vegetation mats. When not basking, radiotracked E. murinus were almost always partially or entirely concealed by fallen logs, detritus, submerged vegetation, inside fallen Mauritia flexuosa trunks, beneath undercut banks, and on one occasion within a burrow of unknown origin. Based on our field observations, basking behaviour should be an important factor for evaluating population densities of this species using survey detection methods in Amazon rainforest systems.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".