Biochar applications to boreal podzol improve soil hydraulic properties and control nitrogen dynamics
Bibliographic record
Abstract
The study aimed to investigate the effects of biochar (BC) application on hydraulic properties and nitrogen (N) transport in a podzolic soil profile. Soil samples were collected from an agricultural research field in Pasadena, Newfoundland, Canada. The following three types of leaching columns were prepared: ( i) topsoil, ( ii) top and E-horizon soil, and ( iii) mixed soil (2:1 ratio of topsoil and E-horizon soil). Granular biochar (GBC) and powder biochar (PBC) were mixed with soils at the rate of 0%, 1% and 2% (w/w). BC’s morphological structure and pore size distribution were examined using a scanning electron microscope, and the specific surface area was assessed by the Brunauer−Emmett−Teller method. Soil physical and hydraulic properties (bulk density, porosity, field capacity (FC), permanent wilting point, plant available water (PAW)), leaching concentration of nitrate (NO3 −) and ammonium (NH4 +), and volume of leachate were measured through a total of 378 experiments under laboratory conditions. GBC and PBC showed hydrophobic and hydrophilic characteristics, respectively. With the 2% PBC amendment, porosity increased by 3%, FC by 10%, and PAW by 13% in the mixed soil and reduced NO3 − leaching by 36% in top and E-horizon soil and NH4 + leaching by 72% in mixed soil. On the other hand, NO3 − and NH4 + leaching was reduced by 26% and 33% in mixed soil when treated with 2% GBC. A 2% application rate for both BC (GBC and PBC) showed the best performance to enhance soil hydraulic properties and retain significant amounts of NO3 − and NH4 + in the boreal podzol.
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 | 0.000 | 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) | 0.000 | 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".