Bibliographic record
Abstract
GreenFILE is an EBSCO database of scholarly, government, news, and general-interest titles covering renewable energy, green building (including net-zero construction), global warming, pollution, recycling, and sustainability. GreenFILE offers extensive coverage of records, including indexing and abstracting services, in sustainability and other areas. The database contains more than one million records, with open access offered for more than 15,000 additional records. Searchable fields include standard fields, such as Abstract, Accession Number, and Author, as well as value-added fields, such as Cover Story, Author Supplied Abstract, and Document Type. GreenFILE offers both Basic and Advanced Search, featuring enhanced Boolean capabilities. Browsers or searchers who are new to environmental subject searching will find aids such as the Definition of Fields Table, Publications Authority File, and Thesaurus Authority File helpful in understanding the scope of the subjects and holdings afforded by this database. GreenFILE offers numerous tutorials designed for every level of researcher, from entry level to advanced.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.607 | 0.554 |
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".