Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An obstacle for the implementation of climate adaptation projects is access to<br/>cheap financing. In this report, we review various solutions, all of which have<br/>their advantages and disadvantages. The described financing models can be<br/>combined into hybrids that are adapted to the specific context.<br/>The current financial conditions make it possible to take out very inexpensive<br/>loans. A prerequisite for the loans for climate adaptation projects to be affordable<br/>is that the borrowers are assessed to have a high credit rating. In this<br/>connection, municipalities and the state will ensure high creditworthiness by<br/>guaranteeing the loans. To the extent that municipalities and the utility company<br/>take responsibility for climate adaptation projects, the current financing<br/>option through KommuneKredit is attractive.<br/>The report reviews two foreign financing examples. We show that financing<br/>models in Germany, Canada, and Denmark are very different. For example,<br/>coastal protection in Germany is locked into dike solutions as the federal and<br/>local state funds up to 90 % of sea wall construction. In Canada, municipalities<br/>- and similar administrative entities - can apply for co-financing in funds paid<br/>for by the federal and local governments. Both the German and the Canadian<br/>solutions can serve as an inspiration and as a warning in relation to the developmentof new financing models in Denmark.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.014 |
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 it