Fine root turnover in a northern hardwood forest: a direct comparison of the radiocarbon and minirhizotron methods
Bibliographic record
Abstract
We examined fine root turnover using both the minirhizotron and radiocarbon methods within the organic horizon of a northern hardwood forest to better understand discrepancies in turnover estimates obtained using these methods. The recently developed radiocarbon method estimates the mean age of organic matter by comparing its radiocarbon content to recorded atmospheric radiocarbon levels, which peaked in the 1960s as a result of thermonuclear weapons testing. The radiocarbon content of fine roots harvested from minirhizotron tubes did not differ from that of roots collected from the soil, suggesting these two methods sampled the same population of fine roots. However, long-term observation of fine root survivorship using minirhizotrons showed that root age distribution is positively skewed, causing systematic overestimation of fine root turnover by the minirhizotron method and underestimation by the radiocarbon method. We developed a parametric regression model of fine root survivorship. Our estimate of fine root turnover (about 30% per year) using this variation of the minirhizotron method was supported by radiocarbon data considered in conjunction with fine root age distribution.
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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 | 0.001 |
| 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) | 0.000 | 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".