Application of herb chronology: Annual fertilization and climate reveal annual ring signatures within the roots of US tallgrass prairie plants
Bibliographic record
Abstract
The relatively new field of herbaceous root chronology (“herb chronology”) uses the annual rings of secondary xylem in roots of perennial forbs to analyze belowground secondary growth as a function of annual growth environment. By using three tallgrass forb species from long-term experiments within Konza prairie of northeastern Kansas (USA), we aimed to find the effects of fertilization, growing season temperature, and precipitation on annual secondary growth. For two of the three species, we found annual rings were significantly larger among plots that were fertilized annually with phosphorus or nitrogen + phosphorus in contrast to unfertilized control plots. Rings also had significant variation with climatic variables. We found a consistent negative correlation with early season temperature for each species. However, early growing season precipitation proved to be far less consistent, with positive correlations only found in a few cases between species. Overall, we conclude that annual rings in these select tallgrass prairie species may not carry reliable climatic signatures; rather site-specific ecological factors, such as aboveground competition with neighbors, may be more important for annual ring patterns. In our discussion we propose a framework to help better disentangle the effects of site or climatic factors that may affect herbaceous annual ring variation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".