Seasonality and moisture regime control soil respiration, enzyme activities, and soil microbial biomass carbon in a semi-arid forest of Delhi, India
Bibliographic record
Abstract
Abstract Soil respiration, soil enzymes, and microbial biomass are important in carbon cycling in the terrestrial ecosystem which is generally limited by environmental factors and soil carbon availability. Hence, we tried to assess the factors affecting the functional aspects of these processes in a semi-arid climate. We monitored soil respiration (surface) using a portable infrared gas analyzer (Q-Box SR1LP Soil Respiration Package, Qubit Systems, Canada) equipped with a soil respiration chamber (Model: G 180). Soil respiration was measured at midday during each season throughout the study period. Soil enzymatic activities and microbial biomass carbon (MBC) were analyzed following the standard protocol for a year during peak time in four seasons at 0–10 cm and 10–20 cm depth. Soil respiration shows significant variation with highest in monsoon (3.31 μmol CO2 m−2 s−1) and lowest in winter (0.57 μmol CO2 m−2 s−1). Similarly, β-glucosidase, dehydrogenase, and phenol oxidase activity ranged from 11.15 to 212.59 μg PNP g−1 DW h−1, 0.11 to 16.47 μg TPF g−1 DW h−1, and 4102.95 to 10187.55 μmol ABTS+ g−1 DW min−1, respectively. MBC ranged from 17.08 to 484.5 μg C g−1. Besides, soil respiration, soil enzymes (except β-glucosidase), and MBC were significantly correlated with soil moisture. Seasonality, optimum moisture and temperature played a significant role in determining variations in soil microbiological processes (except β-glucosidase activity); the carbon cycling in the study area is assisted by enzyme activity; dehydrogenase and phenol oxidase played a significant role in soil respiration; hence, this landscape is sensitive to environmental changes.
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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 | 0.001 | 0.001 |
| 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".