A standardized protocol for the determination of specific leaf area and leaf dry matter content
Bibliographic record
Abstract
Summary The impact of sample preparation, rehydration procedure and time of collection on the determination of specific leaf area (SLA, the ratio of leaf area to leaf dry mass) and leaf dry matter content (LDMC, the ratio of leaf dry mass to fresh mass) of mature leaves was studied in three wild species growing in the field, chosen for their contrasting SLA and LDMC. Complete rehydration was achieved 6 h after samples were placed into water, but neither of the procedures tested – preparation of samples before rehydration or temperature applied during rehydration – had a significant effect on the final values of SLA or LDMC. As expected, water‐saturated leaves had a lower LDMC than non‐rehydrated leaves; more surprisingly, their SLA was also higher. The impact of rehydration on SLA was especially important when the SLA of the species was high. There was no significant effect of time of sampling on either trait in any species over the time period covered (09·00–16·30 h). These results suggest that SLA and LDMC obtained on water‐saturated leaves (SLA SAT and LDMC SAT ) can be used for species comparisons. We propose a standardized protocol for the measurement of these traits. This would allow for better consistency in data collection, a prerequisite for the constitution of large databases of functional traits.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".