Pre-dispersal strategies by Quercus schottkyana to mitigate the effects of weevil infestation of acorns
Bibliographic record
Abstract
We investigated how pre-dispersal strategies may mitigate the effects of weevil infestation of acorns in a population of Quercus schottkyana, a dominant oak in Asian evergreen broad-leaved forests, and assess if weevil infestation contributes to low seedling recruitment. We counted the number of acorns produced, daily from the end of August to mid-late November for 9 years from 2006-2014. We also recorded the rate of acorn infestation by weevils and acorn germination rates of weekly collections. Annual acorn production was variable, but particularly low in 2011 and 2013. There was no trade-off between acorn production and acorn dry mass. However, acorns produced later in the season were significantly heavier. For most years: (i) the rate of weevil infestation was negatively density dependent (a greater proportion of acorns died with increased acorn density), (ii) the percentage germination of acorns was positively density dependent (proportionately more acorns germinated with increased density), and (iii) as the season progressed, the percentage of infested acorns declined while germination rates increased. Finally, (iv) maximum acorn production, percentage infestation and percentage germination were asynchronous. Although pre-dispersal mortality is important it is unlikely to be the primary factor leading to low recruitment of oak seedlings.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".