Variation in photosynthetic properties among bog plants
Bibliographic record
Abstract
Plant functional types (PFTs) are used to make generalizations in modeling how plants impact ecosystem functioning. In boreal bogs the number of plant species is small, but several PFTs are represented, namely sedges, deciduous and evergreen dwarf-shrubs, as well as hummock, lawn, and hollow Sphagna. Despite the use of PFTs in modeling, the value of PFTs to describe the photosynthetic properties of bog plants has not been systematically studied. We aim to quantify the photosynthetic properties of typical bog plant species and assess how well PFT divisions reflect differences among species. We measured photosynthetic light response and physiological state of photosystem II of 19 species, monthly, over a growing season. Differences were assessed using principal component analysis and mixed models. Photosynthetic properties separated Sphagna into traditional PFTs, of which hollow species had the highest gross photosynthesis. Sphagnum photosynthesis had large seasonal variation, as monthly differences exceeded those among PFTs or species. The photosynthetic properties of vascular plants differed widely among species but did not follow traditional PFTs. Vascular plant seasonal changes were of less importance than interspecific differences. The results justify using PFTs to describe the ability of bog Sphagna to bind carbon, but do not justify the same approach for vascular plants.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".