Announcement—guidance document for acquiring reliable data in ecological restoration projects
Bibliographic record
Abstract
The Laurentian Great Lakes are undergoing intensive ecological restoration in Canada and the United States. In the United States, an interagency committee was formed to facilitate implementation of quality practices for federally funded restoration projects in the Great Lakes basin. The Committee's responsibilities include developing a guidance document that will provide a common approach to the application of quality assurance and quality control (QA/QC) practices for restoration projects. The document will serve as a “how‐to” guide for ensuring data quality during each aspect of ecological restoration projects. In addition, the document will provide suggestions on linkingQA/QCdata with the routine project data and hints on creating detailed supporting documentation. Finally, the document will advocate integrating all components of the project, includingQA/QCapplications, into an overarching decision‐support framework. The guidance document is expected to be released by the U.S.EPAGreat Lakes National Program Office in 2017.
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 | 0.028 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.097 | 0.107 |
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".