The effect of habitat selection on offspring performance in the giant water bug <i>Belostoma bifoveolatum</i>
Bibliographic record
Abstract
The selection of breeding habitat is crucial for many ectotherms inhabiting aquatic environments. Giant water bugs offer an excellent model for analysis of how temperature affects breeding habitat selection and reproductive success. This work focuses on whether wetland temperature influences habitat selection and offspring success in the giant water bug Belostoma bifoveolatum Spinola, 1852. To determine breeding habitat characteristics of B. bifoveolatum, 21 wetlands lying on the environmental gradients of canopy cover and altitude were sampled in spring; water temperature was monitored and the presence or absence of adult belostomatids, incubating males, and nymphs were recorded. Several environmental variables were measured in sites where males incubated eggs and were compared with those from control sites. Field experiments were also conducted to evaluate the effect of site choice on hatching success. Belostoma bifoveolatum was found up to 1545 m above sea level, but reproduction was observed only in the warmest temporary wetlands, with low forest cover, from 300 to 1000 m above sea level. Incubating males were found in the shallowest and warmest sites within the wetlands where egg incubation time was shortest and hatching success highest. The selection of breeding habitat and incubation site, along with parental care, constitute important adaptations in B. bifoveolatum and could explain its success in this cold region.
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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".