A handbook for the standardised sampling of plant functional traits in disturbance-prone ecosystems, with a focus on open ecosystems
Bibliographic record
Abstract
Plant functional traits provide a valuable tool to improve our understanding of ecological processes at a range of scales. Previous handbooks on plant functional traits have highlighted the importance of standardising measurements of traits to improve our understanding of ecological and evolutionary processes. In open ecosystems (i.e. grasslands, savannas, open woodlands and shrublands), traits related to disturbance (e.g. herbivory, drought, and fire) play a central role in explaining species performance and distributions and are the focus of this handbook. We provide brief descriptions of 34 traits and list important environmental filters and their relevance, provide detailed sampling methodologies and outline potential pitfalls for each trait. We have grouped traits according to plant functional type (grasses, forbs and woody plants) and, because demographic stages may experience different selective pressures, we have separated traits according to the different plant life stages (seedlings saplings and adults). We have attempted to not include traits that have been covered in previous handbooks except for where updates or additional information was considered beneficial.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.064 | 0.047 |
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".