Relevance of collected juveniles to the analysis of spider communities
Bibliographic record
Abstract
Spider field collections often consist of a high percentage of immature specimens that are not identifiable to species; in many studies these juveniles are discarded and not used in analyses. To evaluate if this practice affects the results of a community study, we sampled foliage-dwelling spiders in two habitats, reared the collected immature spiders until maturity, and identified them to species. We tested if measurements of species richness, evenness, and assemblage composition changed with the exclusion of data from immature specimens by analyzing two datasets: one including mature spiders only, the other including both mature and immature spiders (complete dataset). Nine of the total 49 spider species were collected only as juveniles, but only one of these nine species, Philodromus praelustris Keyserling 1880, was common (≥ 10% of collection). The distribution of individuals among species was more even in the complete dataset than the mature-only dataset, which could either indicate differences in composition or reflect sampling effort. However, species richness estimates were similar regardless of dataset, and there were only small changes in species composition of the samples between datasets, suggesting that there were not important compositional differences between the samples in each dataset. The inclusion of immature spiders in the data in this study yielded the same results that would occur with increased sampling effort.
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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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".