Above and belowground carbon pools are affected by dominant floral species in hyper-arid environments
Bibliographic record
Abstract
<ns4:p><ns4:bold>Introduction: </ns4:bold>Carbon (C) pools in desert ecosystems have not been well investigated, especially in relation to quantitative assessment for different compartments. In many ecosystems C uptake may increase, which leads to accelerated C cycling belowground.</ns4:p><ns4:p> <ns4:bold>Methods: </ns4:bold>Therefore there is a strong need for C storage in compartments such as phytomass and/or within soils. In the present study we assessed C pools of different soil/vegetation associations as affected by the dominant tree and shrub species.</ns4:p><ns4:p> <ns4:bold>Results:</ns4:bold> Mountain valleys had the highest C pool in the phytomass compartment with an average of 3.6 tons per hectare, of which 1.32 tons per hectare were contained aboveground. The introduced<ns4:italic> Prosopis juliflora</ns4:italic> had by far the highest average contribution of 3.47 tons of C per hectare. Most of which is in the above ground parts (83.3%) and the remaining is sequestered below ground. <ns4:italic>Halopeplis perfoliata</ns4:italic>, however, contributed the least C to the desert systems of the UAE. Some land forms, such as mountain valleys, were shown to sequester more C than others, which constitute a good reason to improve their conditions.</ns4:p><ns4:p> <ns4:bold>Conclusions: </ns4:bold>Few shrub/tree species, such as <ns4:italic>P. juliflora</ns4:italic>, were also reported to have high potentials as a C pool in the hyper-arid environment of the UAE.</ns4:p>
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".