Bibliographic record
Abstract
Abstract Background The below‐ground component of vegetation accounts for the bulk of plant mass and vegetation function (e.g. carbon sequestration) in temperate ecosystems, yet the proportion of plant ecology studies that consider roots is <20%. Methods I review how minirhizotron technology and DNA sequencing of mixed‐species root samples allows new insights into below‐ground vegetation structure and function. Results Recent advances highlight important differences between the below‐ and above‐ground parts of vegetation. For example, plant species richness below ground is about 50% greater than that above ground. Below‐ground plant richness has been measured from only a few sites, and patterns along gradients of productivity and life‐form turnover are unknown. Fine roots differ from leaves in temperate ecosystems by having a growing season 40% longer, and by persisting over multiple growing seasons. Aspects of roots other than growth may vary seasonally, such as nutrient uptake, competition with microbes, or mycorrhizal hyphal production or activity. Minirhizotrons allow the investigation of root heterogeneity at very small scales (<1 mm) that may be more relevant to fine roots and rhizospheres than data obtained from larger‐scale soil sampling. Conclusions Work in the near future promises a more complete picture of vegetation function by elucidating mechanisms within the bulk of vegetation, below ground.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".