Replacing qualitative life‐history traits by quantitative indices in parasitoid evolutionary ecology
Bibliographic record
Abstract
Abstract Life‐history traits, which describe the various aspects of the life cycle of a species, can be either quantitative or qualitative. Quantitative traits are likely to adapt to gradual changes in the environment of a species, whereas qualitative traits, which refer to traits that are discontinuous in their variation, pose constraints on the evolution of a species. Traits that are described as qualitative may indeed represent discontinuous characteristics or they can be the result of an oversimplification in the description of the life history of a species. The ovigeny index, which describes the temporal distribution of egg production for a species, has replaced a qualitative life‐history trait and has been an important contribution in our understanding of the reproductive ecology of insect parasitoids. We propose here that several other qualitative life‐history traits, currently used to describe the evolutionary ecology of insect parasitoids, could advantageously be replaced by quantitative life‐history traits. Although replacing these qualitative life‐history traits will require devising indices that are biologically and ecologically meaningful, the potential gain in our understanding of the evolutionary forces that have shaped the diversity of life‐history strategies of insect parasitoids is important and would fully warrant this effort.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".