Switchgrass Cultivation within Loblolly Pine Plantations Influences Invertebrate Community Composition and Resource Use
Bibliographic record
Abstract
Global interest in biofuels has resulted in the development of novel land-use practices for the production of cellulosic biomass. One novel land-use practice that has recently been developed is intercropping switchgrass (Panicum virgatum) between rows of planted loblolly pines (Pinus taeda). However, our understanding of how intercropping switchgrass influences loblolly pine flora and fauna is limited. Therefore, we evaluated the influence of switchgrass cultivation within loblolly pine stands on invertebrate communities. We detected 2,913 individuals (n = 1,172 and 1,741 in 2014 and 2015, respectively), encompassing 13 orders. To examine invertebrate community composition among treatments, we conducted metric multidimensional scaling (MDS) in R. Multivariate analysis determined that treatment had a significant (Pr(>r) = 0.01) influence on invertebrate communities. Furthermore, stable isotope analysis suggests that Orthopterans are not assimilating cultivated switchgrass (C4 species), but are instead assimilating C3 plant species, such as Rubus argutus (sawtooth blackberry). Results indicate that switchgrass intercropping may be a viable land-use practice for the co-production of cellulosic biomass and forest products and the maintenance of invertebrate communities associated with loblolly pine plantations.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".