Predatory behaviour and prey-capture decision-making by the web-weaving spider <i>Micrathena</i> <i>sagittata</i>
Bibliographic record
Abstract
Optimal-foraging theory predicts how a predator would feed most efficiently when faced with a choice of two types of prey differing in profitability and density in the habitat. The predator should focus only on the more profitable prey, since any prey item eaten by the predator has a cost in terms of the time and resources taken to subdue and eat it. A study of the hunting behaviour and prey-type selection of the web-weaving spider Micrathena sagittata (Walckenaer, 1841) (Araneae: Araneidae) in the field is documented. In the first part of the study, prey of two sizes were offered in four sectors of the web (top, bottom, right, and left). A prey item was provided at one position of the web at a time. Attack time was recorded at each position. Also, choice and no-choice tests were carried out by offering prey in opposing web sectors (top and bottom) simultaneously. Large prey were more successfully captured in the upper parts than in the bottom parts of the web. In the choice test, spiders always preferred large prey to small prey, while in the no-choice test, spiders always responded to the first stimulus received. Two different attack strategies, depending on prey size, were observed. Hunting strategies and prey-size preference can be related to the cost of web construction and profitability of the prey type.
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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) | 0.001 | 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".