Toward Synthesis of Relationships among Leaf Longevity, Instantaneous Photosynthetic Rate, Lifetime Leaf Carbon Gain, and the Gross Primary Production of Forests
Bibliographic record
Abstract
The assimilation of carbon by plant communities (gross primary production [GPP]) is a central concern in plant ecology as well as for our understanding of global climate change. As an alternative to traditional methods involving destructive harvests or time-consuming measurements, we present a simple, general model for GPP as the product of the lifetime carbon gain by a single leaf, the daily leaf production rate, and the length of the favorable period for photosynthesis. To test the model, we estimated leaf lifetime carbon gain for 26 species using the concept of mean labor time for leaves (the part of each day the leaf functions to full capacity), average potential photosynthetic capacity over the leaf lifetime, and functional leaf longevity (leaf longevity discounted for periods within a year wholly unfavorable for photosynthesis). We found that the lifetime carbon gain of leaves was rather constant across species. Moreover, when foliar biomass was regressed against functional leaf longevity, aseasonal and seasonal forests fell on a single line, suggesting that the leaf production rate during favorable periods is not substantially different among forests in the world. The gross production of forest ecosystems then can be predicted to a first approximation simply by the annual duration of the period favorable for photosynthetic activity in any given region.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".