Bibliographic record
Abstract
Summary Trait‐based approaches have led to significant advances in plant ecology, but are currently biased toward above‐ground traits. It is becoming clear that a stronger emphasis on below‐ground traits is needed to better predict future changes in plant biodiversity and their consequences for ecosystem functioning. Here I propose six ‘below‐ground frontiers’ in trait‐based plant ecology, with an emphasis on traits governing soil nutrient acquisition: redefining fine roots; quantifying root trait dimensionality; integrating mycorrhizas; broadening the suite of root traits; determining linkages between root traits and abiotic and biotic factors; and understanding ecosystem‐level consequences of root traits. Focusing research efforts along these frontiers should help to fulfil the promise of trait‐based ecology: enhanced predictive capacity across ecological scales. Contents Summary 1597 I. The promise of trait‐based plant ecology 1597 II. Redefining fine roots 1597 III. Quantifying trait dimensionality 1598 IV. Integrating mycorrhizas 1598 V. Broadening the suite of below‐ground traits 1600 VI. Determining trait–environment linkages 1601 VII. Understanding ecosystem‐level consequences 1601 VIII. Conclusions 1601 Acknowledgements 1602 References 1602
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".