Horticultural additives influence peat biogeochemistry and increase short-term CO2 production from peat
Bibliographic record
Abstract
Abstract Aims Peat is used as a major ingredient of growing media in horticulture. Peat extracted from bogs can be acidic and low in nutrient availability and is therefore mixed with liming agents, nutrients, surfactants, perlite and so on. This study aims to estimate the rates at which raw peat and the modified peat (‘growing media’) decompose to release carbon dioxide (CO2), to estimate the release of carbon (C) from liming agents and to estimate how peat biogeochemistry is changed. Methods We obtained 28 and 24 samples of raw peat and 24 growing media from four peat extraction companies in Canada. Growing media were treated with horticultural additives. We incubated the samples under laboratory conditions, measuring CO2 production, tracer using $${\updelta }^{13}{\text{C}}$$ δ 13 C - $${{\text{CO}}}_{2}$$ CO 2 , pH, C, nitrogen (N) content and humification indices (HIs) from infrared technology called Fourier transform-mid infrared (FT-MIR). Results C:N ratio, pH, dissolved organic carbon, bulk density and C content differed significantly (P < 0.05) between raw peats and growing media. There was more than a doubling of total $${{\text{CO}}}_{2}$$ CO 2 production from growing media compared to raw peat. HIs show higher values for the growing media, which could result from spectral band shifts in the growing media because of increased cation availability. $${\updelta }^{13}{\text{C}}$$ δ 13 C - $${{\text{CO}}}_{2}$$ CO 2 as a tracer showed an average 22% of the total $${{\text{CO}}}_{2}$$ CO 2 production orginated from added carbonate materials. Conclusion Our results provide the rates (0.15 ± 0.017mgCO2-Cg−1d−1) at which horticultural peat decomposes and on the source of emitted $${{\text{CO}}}_{2}$$ CO 2 . This will improve current estimates CO2 emissions from horticultural peat.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".