Former ecosystem type and soil organic matter regulate the effects of reforestation on soil bulk density, porosity, electrical conductivity, and texture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reforestation is globally promoted and significantly impacts soil properties, yet a comprehensive global assessment of its effects on soil physical properties is lacking. This study aimed to assess the global patterns and drivers of reforestation impacts. We analyzed 7890 paired observations from 455 publications across the globe to quantify reforestation’s impact on soil bulk density (BD), porosity, electrical conductivity (EC), and textures. We found that (1) reforestation increased total porosity (TP), capillary porosity (CP), clay content, and silt content by 7.90%, 12.22%, 7.48%, and 6.15%, respectively, but decreased BD and EC by 3.66% and 14.29%, respectively (these effects were stronger in barelands and deserts); (2) reforestation effects were significantly affected by former ecosystem and reforestation patterns, with higher improving effects reforestation in barelands and deserts, and with different combinations of tree, shrub, and/or grass, respectively; (3) latitude, stand age, and the logarithmic response ratio (lnRR) of soil organic matter (SOM) positively influenced TP, CP, clay content, and silt content, but negatively affected BD; and (4) former ecosystem type, lnRR of SOM, stand age, latitude, and stand density are the important moderator variables, although their effects differ across BD, TP, CP, EC, sand content, and silt content. Overall, our quantitative analyses clearly show the effects of reforestation on BD, porosity, EC, and textures at a global scale. This is essential for enhancing our understanding and forecasting the effects of reforestation on soil nutrient retention, structure improvement, water retention, under global change in the future.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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 it