Rapid fine root disappearance in a pine woodland: a substantial carbon flux
Bibliographic record
Abstract
Fine root production and mortality are difficult to estimate accurately, because some fine roots die within days of being produced, and many apparently healthy roots disappear rapidly with no obvious period of senescence. Such root dynamics are difficult to analyze without very fine-scaled temporal observations. To capture the behavior of short-lived and rapidly disappearing roots, we sampled minirhizotron tubes weekly for 11 months in a Pinus palustris Mill. woodland. Fine root ([Formula: see text]2 mm diameter) length production and length mortality during this period were 1.57 ± 0.23 mm·cm2(mean ± SE) and 1.19 ± 0.17 mm·cm2, respectively. Depending on the type of estimate used, rapid disappearance accounted for between 21 and 37% of total fine root mortality. Rapidly disappearing roots had relatively short life-spans, a median of just 10.5 days. Monthly sampling of the same data set underestimated length production by 15%, overestimated median root life-span by 60%, and obscured causes of root loss. If short-lived roots are not accounted for, total net primary productivity in temperate forests may be underestimated by as much as 10%. We propose that belowground herbivory is the leading explanation for this rapid disappearance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".