Frugivorous weevils are too rare to cause Janzen–Connell effects in New Guinea lowland rain forest
Bibliographic record
Abstract
Abstract: A community of frugivorous weevils was studied by quantitative rearing of 57 weevil species represented by 10485 individuals from 326 woody plant species in lowland rain forest in Papua New Guinea. Only fruits from 35% of plant species were attacked by weevils. On average, weevils were reared from only 1 in 33 fruits and 1 kg of fruit was attacked by 2.51 individuals. Weevil host specificity was relatively high: 42% of weevil species fed on a single plant genus, 19% on a single plant family and only 16% were reared from more than one family. However, monophagous specialists represented only 23% of all reared individuals. The average 1 kg of fruits was infested by 1.84 individuals of generalist weevils (feeding on allogeneric or allofamilial host species), 0.52 individual of specialists (feeding on a single or several congeneric species), and 0.15 individual of unknown host specificity. Large-seeded fruits with thin mesocarp tended to host specialist species whereas those with thick, fleshy mesocarp were often infested with both specialists and generalists. Weevils tended to avoid small-seeded, fleshy fruits. The low incidence of seed damage (3% of seeds) suggests that weevils are unlikely to play a major role in regulating plant populations via density-dependent mortality processes outlined by the Janzen–Connell hypothesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".