Blev det som det var tänkt? : En internationell kunskapsöversikt om miljöuppföljning av väg- och järnvägsprojekt
Bibliographic record
Abstract
Documenting how far the real environmental effects and consequences agree with those that were described in the environmental impact assessment (EIA) is the main purpose of an environmental follow-up. Another of its purposes is to identify unforeseen effects and consequences, so that appropriate countermeasures can be taken. Describing the extent to which any adaptive or mitigation measures had the desired effect may be yet a further purpose of making an environmental follow-up. An environmental follow-up can also aim to describe whether the environmental consequences of the infrastructure project was kept within the framework laid down at the time the investment decision was made. This overview reports how an EIA follow-up is organised and carried out in other countries, principally Norway, the Netherlands, Germany, Switzerland, the USA, Canada, Brazil, Australia, New Zealand and Hong Kong. Procedures are presented for selecting infrastructure projects to follow up, together with the environmental effects that are to be followed up. The importance of clarifying the purpose of the follow-up is emphasised, as is the importance of the follow-up activities being carried out according to a defined programme. Among other things, the follow-up programme describes the various players' responsibilities, access to baseline data, the timing of the follow-up, the methods to be used, and how the results are to be reported and used. The overview also takes up the follow-up's linkage to an environmental management system. Examples are also given of a method known as adaptive environmental management. Finally, the review looks at how experience gained from follow-ups can be disseminated and transferred to the planning of future infrastructure projects.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.020 |
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".