Female false black widow spiders,<i>Steatoda grossa,</i>recognize webs based on physical and chemical cues
Bibliographic record
Abstract
Abstract Females of the false black widow,Steatoda grossaCL Koch (Araneae: Theridiidae), invest significant energy and time weaving cobwebs. We tested the hypothesis thatS. grossafemales select sites for their webs based, in part, on the presence of con‐ or heterospecific webs, sensing both physical and chemical web cues. In bioassays, we offered femaleS. grossaa choice between an empty control frame and a frame bearing the web of a conspecific female or that of a female common house spider,Parasteatoda tepidariumCL Koch (Araneae: Theridiidae), recording (1) the time she spent, and (2) the time she spent inactive (a proxy for settling behaviour) on each frame. We also tested the effect of (1) silk micro‐ and macrostructure (wrapped‐up silk or intact web, each semiochemical‐deprived), (2) plastic webs, and (3) silk semiochemical extract on the responses ofS. grossafemales. Females settled on both con‐ and heterospecific webs and chose test stimuli based on their chemical and physical characteristics. Even plastic webs in cobweb‐like arrangement readily prompted settling behaviour by females. Our results suggest that web architecture, rather than web silk, mediates settling responses by femaleS. grossaon pre‐existing webs which may provide structural support for a new web and indicate habitat suitability.
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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) | 0.002 | 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".