Does the Date of Burning Affect Carbon and Nutrient Losses in a Humid Savanna of West Africa?
Bibliographic record
Abstract
A significant impact of fires is the transfer to the atmosphere of the elements contained in combusted biomass. This study was carried out in the Guinean savanna of Lamto (Côte d’Ivoire) to evaluate fire-mediated carbon and nutrient losses from biomass according to the date of burning. The fire regimes tested consisted of three different burning dates: early (EF), mid (MF), and late (LF) season fires, on nine 0.5 ha plots. Carbon and five elemental nutrients were assessed in the aboveground biomass prior to burning and in ash and unburnt biomass after fires; losses were assessed by subtraction. The proportion of nutrients transferred to the atmosphere varied from 42 % (K) to 98 % (C). The lowest losses were recorded during the EF and the greatest during the MF and LF. Emission of CO2 was relatively greatest during the EF than during the MF and LF (16, 14 and 13 t ha-1 respectively). The proportion of the fine ash fraction (< 1 mm) was the greatest during MF. The highest concentrations of K, P, Ca and Mg occurred in this fraction, most susceptible to losses due to wind and rainfall. The percentage losses of C, N and K were positively correlated with fire maximal temperatureand flame height, explaining the relatively higher losses during MF. Over the long term, the MF applied annually, would lead to important depletion of soil nutrients, particularly N and P, which contributes to the low availability of nutrients in the soils of Lamto savannas.
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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 | 0.001 |
| 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 | 0.000 | 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".