Glasshouse vs field experiments: do they yield ecologically similar results for assessing N impacts on peat mosses?
Bibliographic record
Abstract
• Peat bogs have accumulated more atmospheric carbon (C) than any other terrestrial ecosystem today. Most of this C is associated with peat moss (Sphagnum) litter. Atmospheric nitrogen (N) deposition can decrease Sphagnum production, compromising the C sequestration capacity of peat bogs. The mechanisms underlying the reduced production are uncertain, necessitating multifactorial experiments. • We investigated whether glasshouse experiments are reliable proxies for field experiments for assessing interactions between N deposition and environment as controls on Sphagnum N concentration and production. We performed a meta-analysis over 115 glasshouse experiments and 107 field experiments. • We found that glasshouse and field experiments gave similar qualitative and quantitative estimates of changes in Sphagnum N concentration in response to N application. However, glasshouse-based estimates of changes in production--even qualitative assessments-- diverged from field experiments owing to a stronger N effect on production response in absence of vascular plants in the glasshouse, and a weaker N effect on production response in presence of vascular plants compared to field experiments. • Thus, although we need glasshouse experiments to study how interacting environmental factors affect the response of Sphagnum to increased N deposition, we need field experiments to properly quantify these effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".