Impact of land-use change to biomass crops and biofertilizer application on biomass productivity, soil organic carbon, nitrogen, phosphorus and soil health
Bibliographic record
Abstract
This dataset is compiled to investigate the effects of land-use change on biomass crops and the application of biofertilizers, focusing on biomass productivity, soil organic carbon (SOC), nitrogen, phosphorus, and overall soil health. The data in this collection aims to quantify SOC sequestration rates using 2016 baseline data, assess soil total nitrogen and phosphorus in different land-use systems, measure SOC and nitrogen in various soil aggregate-size fractions, evaluate greenhouse gas emissions influenced by applied fertilizer treatments, determine carbon dioxide (CO2) released from different fractions through incubation studies, and analyze SOC stability and sustainability across three land-use systems. Additionally, the dataset aims to quantitatively assess biomass yields influenced by different biofertilizers provided by industry partners, ranking them based on their yield response and cost-effectiveness. The dataset also contributes to the development of soil health indicators for biomass crops by quantitatively assessing fungal communities (micro-faunal), earthworm densities (macro-faunal), SOC, Haney soil health, and Solvita test.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
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".