Black carbon in wildfire‐affected shrubland Mediterranean soils
Bibliographic record
Abstract
Abstract Because Mediterranean ecosystems are prone to fire, their soils are expected to contain relevant amounts of black carbon (BC); nevertheless, quantitative information is scarce. Herein, we provide data on the abundance of BC in the surface soil (uppermost 5 cm) of shrubland plots on old agricultural fields diversely affected by fires (0, 1, or 2 wildfires in the last 25 y) and with contrasted land‐use histories (either never cropped, early abandoned, or recently abandoned). Black C and black nitrogen (BN) were quantified in the surface horizon (0–5 cm) as the residue of low‐temperature dichromate oxidation, after previous destruction of mineral matter with HF. The obtained amounts of BC ranged from 0.73 to 10.32 g (kg dw)–1 (mean: 3.07, which corresponds to an average of 8.62% of the total organic C), while the amounts of BN ranged from 21.5 to 373.0 mg (kg dw)–1 (mean: 97.1, or an average of 4.30% of the total N of the samples). Repeated fires did not consistently increase either the BC or the BN amounts. Black‐C and (especially) BN accumulation seems related to fine silt, whereas the effect of clay is unclear. Even though the amounts of BC obtained in this study are slightly higher than those from other ecosystems, including Mediterranean broad‐leaved forests, overall they are far from the very high values reported in the literature for chernozems from Germany or Canada. Thus, on the whole, in Mediterranean shrublands affected by wildfires, BC does not seem to be a dominant fraction in the soil organic C.
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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 | 0.000 |
| 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 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".