The biology of seed discrimination and its role in shaping the foraging ecology of carabids: A review
Bibliographic record
Abstract
Species of carabid (ground) beetles are among the most important postdispersal weed seed predators in temperate arable lands. Field studies have shown that carabid beetles can remove upwards of 65%-90% of specific weed seeds shed in arable fields each year. Such data do not explain how and why carabid predators go after weed seeds, however. It remains to be proven that weed seed predation by carabids is a genuine ecological interaction driven by certain ecological factors or functional traits that determine interaction strength and power predation dynamics, bringing about therefore a natural regulation of weed populations. Along these lines, this review ties together the lines of evidence around weed seed predation by carabid predators. Chemoperception rather than vision seems to be the primary sensory mechanism guiding seed detection and seed selection decisions in carabid weed seed predators. Selection of weed seeds by carabid seed predators appears directed rather than random. Yet, the nature of the chemical cues mediating detection of different seed species and identification of the suitable seed type among them remains unknown. Selection of certain types of weed seeds cannot be predicted based on seed chemistry per se in all cases, however. Rather, seed selection decisions are ruled by sophisticated behavioral mechanisms comprising the assessment of both chemical and physical characteristics of the seed. The ultimate selection of certain weed seed types is determined by how the chemical and physical properties of the seed match with the functional traits of the predator in terms of seed handling ability. Seed density, in addition to chemical and physical seed traits, is also an important factor that is likely to shape seed selection decisions in carabid weed seed predators. Carabid responses to seed density are rather complex as they are influenced not only by seed numbers but also by trait-based suitability ranks of the different seed types available in the environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".