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
Kabisch et al. (2017) reviewed our call for advances in ecosystem service (ES) decisionsupport tools from an urban perspective, and explored how the three research frontiers we identified should be considered in cities. We appreciate how they build on our original ideas, and welcome this as a good example of how the general principles we developed in the original paper can be applied and adapted to specific contexts. In fact, we believe that similar points about the importance of adapting our general principles for specific social-ecological systems could be made for many other systems, such as marine ecosystems or managed forestry systems. The specific characteristics of these different systems also provide opportunities to expand on current ES knowledge and improve ES management tools. For example, as Kabisch et al. (2017) point out, cities are unique due to their relatively small area and high population density, which may make them more ideal than other systems for understanding certain aspects of the linkages between humans and nature and for implementing this understanding in management tools. We take the opportunity to respond to the ideas presented by Kabisch et al. and thus continue the conversation around urban ES.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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