Prey preferences of<i>Phaeacius malayensis</i>, a spartaeine jumping spider (Araneae: Salticidae) from Singapore
Bibliographic record
Abstract
Phaeacius malayensis (Salticidae: Spartaeinae) is an atypical jumping spider that routinely includes other jumping spiders in its diet. This paper presents the first detailed study of P. malayensis' prey preferences. Three basic types of tests of prey preference were used: different types of prey were provided on alternate days (alternate-day tests), two types of prey were provided simultaneously (simultaneous-presentation tests), and an alternative prey type was provided while the predator was already feeding (alternative-prey tests). In alternate-day and simultaneous-presentation testing, but not in alternative-prey testing, (i) P. malayensis preferred spiders (salticids and hunting spiders) to insects; (ii) P. malayensis preferred salticids to hunting spiders; (iii) these preferences were not exhibited in some paired choices when the background was nonmatching, indicating that visual concealment is important with respect to some, but not all, prey; (iv) P. malayensis' prey preferences were not affected by a prior 14-day period without food; (v) after 21 days of fasting, P. malayensis took different types of prey indiscriminately. The adaptive significance of preferences for particular types of prey is discussed in the context of optimal-foraging theory.
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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".