Capacity of the Nearby Infrared Spectroscopy (NIRS) Red for the Prediction of some Properties of Burned Soils in a Semi-Arid Area of Western Algeria
Bibliographic record
Abstract
The forestry sector in Algeria is confronted for the years 1965 to a resurgence of the fires that destroy averaged 48 000 ha per year, or 12% of forest areas. As a result of repeated fires, a pyrophyte vegetation develops on degraded soils and from which the spontaneous regeneration of forest stands and reforestation are very difficult. The Algerian forest sustainable management must therefore be informed by an assessment of the physico-chemical quality of burned soils and the vulnerability of microbial activities in this disturbance Any rehabilitation or regeneration of the burned forest ecosystems program must take into consideration the impact of the fires on the ground [1] and therefore evaluate a large number of soil properties [2]. The main objective of this study was to evaluate the ability of spectroscopy in the near infra-red to predict the time since the last fire and burned some microbiological characteristics of soil. This study is interested in the pine forests of the Algerian semi arid floor Aleppo pines. Promising results have shown a prediction rather senssible of the time since the last fire. The advantages of the SPIR are many. This method is fast, inexpensive and non-destructive.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".