Kumulativa effekter och konsekvenser : behandling i miljöbedömning och miljökonsekvensbeskrivning för vägar
Bibliographic record
Abstract
In Swedish transport infrastructure planning, cumulative effects are not given the attention demanded by the Swedish Environmental Code and the European so-called SEA and EIA directives (Strategic Environmental Assessment and Environmental Impact Assessment, respectively). In the Environmental Code, the formulations concerning cumulative effects are vague. The EIA handbook of the Swedish Road Administration does not give much guidance. There is thus a great need for development of procedures and methods adapted to Swedish road planning. The overall aim of the report is to contribute to the development of the treatment of cumulative effects in SEA and EIA of road planning in Sweden. Specific aims are to describe the concept of cumulative effects and to give advice on approaches and methods that can be used in cumulative effects analysis and assessment. The report mainly builds on American and Canadian literature. “Cumulative effects” are changes to the environment that are caused by an action or measure together with other past, present and future actions and measures. The report advices a structured procedure for the description and assessment of cumulative effects. The recommended procedure takes ”Valued Ecosystem Components” as its point of departure. VEC are any parts of the environment that is considered important by the proponent, public, scientists and authorities participating in the assessment procedure. The procedure has five steps: 1) scoping, 2) analysis of effects, 3) identification of the need for mitigation, 4) evaluation of significance, 5) follow-up. The report gives examples of the application of these steps adapted to Swedish road planning.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".