A plea for a worldwide development of dark infrastructure for biodiversity – Practical examples and ways to go forward
Bibliographic record
Abstract
Artificial light at night (ALAN) has been massively deployed worldwide and has become a major environmental pressure for biodiversity, especially contributing to habitat loss and landscape fragmentation. To mitigate these latter, green and blue infrastructure policies have been developed throughout the world based on the concept of ecological networks, a set of suitable interconnected habitats. However, currently, these nature conservation policies hardly consider the adverse effects of ALAN. Here, we promote the integration of darkness quality within the 'green and blue infrastructure', to implement a ‘dark infrastructure’. Dark infrastructure should be identified, preserved and restored at different territorial levels to guarantee ecological continuities where the night and its rhythms are as natural as possible. For this purpose, we propose an operational 4-steps process that includes 1) Mapping of light pollution in all its forms and dimensions in relation to biodiversity, 2) Identifying the dark infrastructure starting or not from the already identified green/blue infrastructure, 3) Planning actions to preserve and restore the dark infrastructure by prioritizing lighting sobriety and not only energy saving, 4) Assessing the effectiveness of the dark infrastructure with appropriate indicators. Dark infrastructure projects have already been created (for example in France and Switzerland) and can serve as case studies for both urban and natural areas. The deployment of dark infrastructure raises many operational and methodological questions and stresses some knowledge gaps that still need to be addressed, such as the exhaustive mapping of light pollution and the characterization of sensitivity thresholds for model species.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".