An assessment of the threats to the aquatic resources due to rapid growth in the extraction of Shale gas in the US: An application to the Kurdistan region of Iraq
Bibliographic record
Abstract
As of 2015 only four countries in the world are producing Shale gas on a commercial scale. These are Argentina, China, Canada and the US, with the latter producing approximately 90% of the total share. Even though for the case of US, the economic benefits of Shale gas extraction have been substantial, there exists considerably uncertainty in determining its environmental sustainability which is the main motivation behind this work. This effort is accomplished in three steps. In the first step, based on the literature available in the US, a comprehensive assessment of the threats to aquatic resources due to rapid growth in the extraction of Shale gas is carried out. Secondly, to address those identified risks recommendations are proposed to mitigate the adverse impacts. And in the third step, its applicability to Kurdistan region is assessed. Although at present Kurdistan is not producing any Shale gas commercially, the environmental impact studies conducted in the US will aid the Kurdistan government in shaping the future energy policies pertaining to sustainable Shale gas development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".