Learning to Live with Changing Climate and Rising Sea Levels
Bibliographic record
Abstract
In support of the development of the Mitigation and Adaptation Research Institute, MARI, the proposal writing team developed a survey to elicit stakeholder comment. The survey was conducted using the internet-‐based survey tool, Qualtrics, hosted by Old Dominion University and was available from February 26 to March 21, 2014 The survey was distributed to approximately 550 stakeholders who were identified through their participation in previous climate change adaptation events, local government contacts and email lists. Recipients were asked to invite interested colleagues or other contacts to participate in the survey by forwarding the solicitation email. One hundred and eleven responses were received. Most people spent approximately seven minutes on the survey. About a quarter of the respondents were from academia and a third were from federal, state or local government. About 15 percent were from for profit businesses, while 19 percent were from a non-‐governmental organization. The respondents were active in many different governance activities, most prominently policy, planning, research and education. The stakeholders who responded were generally knowledgeable about climate change and sea level rise mitigation and adaptation.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".