Bibliographic record
Abstract
Injection of wastes into the deep subsurface has become a contentious issue, particularly in emerging regions of oil and gas production. Experience in other regions suggests that injection is an effective waste management practice and that widespread environmental damage is unlikely. Over the past several decades, 23 km(3) of water has been injected into the Western Canada Sedimentary Basin (WCSB). The oil and gas industry has injected most of this water but large amounts of injection are associated with mining activities. The amount of water injected into this basin during the past century is 2 to 3 orders magnitude greater than natural recharge to deep formations in the WCSB. Despite this large-scale disturbance to the hydrogeological system, there have been few documented cases of environmental problems related to injection wells. Deep injection of waste appears to be a low risk activity based on this experience but monitoring efforts are insufficient to make definitive statements. Serious uncharacterized legacy issues could be present. Initiating more comprehensive monitoring and research programs on the effects of injection in the WCSB could provide insight into the risks associated with injection in less developed sedimentary basins.
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
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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".