Bibliographic record
Abstract
Abstract Environmental impact assessment (EIA) is a systematic process designed to identify and predict the potential impacts of human activity on the biophysical and human environment. It also functions as an environmental management tool to identify measures to avoid, mitigate or compensate for those effects. EIA is intended to be an iterative process to follow‐up to projects postimplementation to determine actual environmental outcomes, interpret and communicate information about those outcomes and investigate opportunities for improved project environmental performance. Originating from the United States’ National Environmental Policy Act of 1970, EIA is now amongst the most successful and widely practiced environmental management tools in the world. Key Concepts: EIA is an aid to decision making through which concerns about the potential environmental consequences of proposed projects are assessed before those projects become a reality. Screening in EIA ensures that assessments are done when needed and not done when not needed. Scope is essential to good EIA and the goal is to focus on a select set of environmental components that are deemed important from scientific, public or regulatory perspectives and are likely to be adversely affected by the project. The underlying intent of EIA is to allow project proponents, managers and decision makers to enhance the benefits of proposed development projects and to mitigate potentially adverse impacts to the point of acceptability. EIA must be applied early in the development planning processes if it is to be influential in project design and decision making. Information gained in EIA follow‐up studies, after the project is implemented, provides feedback to improve predictions and mitigation and management programmes, and an opportunity to learn for subsequent project proposals.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.102 | 0.027 |
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".