Higher taxa as surrogates of species richness of spiders in insect‐resistant transgenic rice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Biodiversity assessments can often be time‐ and resource‐consuming. Several alternative approaches have been proposed to reduce sampling efforts, including indicator taxa and surrogates. In this study, we examine the reliability of higher taxon surrogates to predict species richness in two experimental rice fields of Fujian Province, southeastern China during 2005 and 2009. Spider samples in transgenic and nontransgenic plots were collected using a suction sampler. Both the genus and family surrogates had significant and positive linear relationships with species richness in the transgenic and nontransgenic rice fields. The rice varieties did not significantly influence the linear relationships. Our findings suggest that higher‐taxon surrogacy could be a useful alternative to complete species inventory for risk assessments of transgenic rice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it