<i>Justicia americana</i> exhibits stem density–aboveground biomass relationships and variability in elemental composition and isotopic signature across tissue types and land use gradients
Bibliographic record
Abstract
Riverine macrophytes are increasingly understood to influence both the physical and chemical dynamics of rivers by creating biogeochemical hotspots and stabilizing benthic sediments. They contribute carbon and nutrients to both aquatic and adjacent terrestrial ecosystems through senescence and herbivory. Here, we report the relationships between Justicia americana biomass, stem length, and stem density, and % carbon (%C), % nitrogen (%N), C:N, δ15N, and δ13C relationships between tissues types (roots, stems, and leaves). Additionally, we report the effects of land use on the plant's tissue. We found that (1) J. americana stem density and dry biomass were positively correlated ( P < 0.001); (2) δ13C and %N were significantly different in leaf and root tissue; (3) δ13C and δ15N were both positively correlated across tissue types; (4) Leaf C:N was less than ½ of root and stem tissue; (5) %C in leaf tissue was positively correlated ( P < 0.05) with forest cover and negatively correlated with urbanization and watershed area; and (6) provide evidence that J. americana utilizes a C3 photosynthetic pathway. Overall, this study provides novel insight into the ecology of J. americana by elucidating the nutritional quality of different plant tissues and stoichiometric changes in the plant in response to land use.
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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".