Sustaining soil carbon in bioenergy cropping systems of northern temperate regions.
Bibliographic record
Abstract
Abstract Soil organic carbon (SOC) has an essential role in controlling ecosystem functions associated with soil physical, chemical and biological properties. Maintaining the SOC pool size in agroecosystems is important to sustain food security, protect soil biodiversity and buffer environmental impacts. The SOC pool is dynamic, with losses occurring due to CO 2 mineralization and gains from microbially mediated humification of organic substrates into stable C compounds. Bioenergy production from lignocellulosic feedstock implies that greater amounts of plant residues will be removed from agroecosystems and could deplete the SOC pool, based on empirical models and experimental results from long-term field trials. In northern temperate regions, several management practices are suggested to conserve the SOC pool, such as the application of biochar, judicious use of organic and inorganic fertilizers, crop rotations that include high biomass producing non-bioenergy crops or intercropping systems that combine perennial bioenergy crops with other crops (annuals or trees). Moreover, new technologies such as genetically modified (GM) bioenergy crops are recommended to enhance bioenergy production per unit energy input. Those modifications include GM crops with higher resource-use efficiency (i.e., for water, nutrients and light), GM crops with cellulase/ligninase enzyme systems for biofuel production and GM crops with higher calorific values that release more energy during combustion.
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 | 0.001 |
| 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".