The top 101 cited articles in environmental clean-up: Oil spill remediation
Bibliographic record
Abstract
The aim of this search was to identify the 101 top cited articles in the field of oil spill remediation. A search was conducted based on a database of the Web of Science included the journal citation reports from 1980 to 2013. The number of citations of the first 101 top cited articles is from 24 to 816. The decades with most top-cited articles published were 2000-2009 (47 articles) and 1990-1999 (37 articles). The most common research area of study was environmental science ecology. All the articles were published in 54 different journals in this category. Journals with the highest number of cited articles were Applied and Environmental Microbiology (10 articles), Environmental Science and Technology (6 articles), Organic Geochemistry (6 articles), Chemosphere (5 articles). Among the top cited articles the mostly named author were Sakkata Y, and Uddin MA with 6 of articles, followed by Fedorak PM with 5. Out of 101 top cited articles, 14, 13 and 12 articles originated from Canada, USA and France, respectively. Okayama and Alberta Universities were the most common productive institutions. Based on our knowledge, this is the first report of the 101 top cited articles in this category.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.046 | 0.067 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".