Bibliographic record
Abstract
Preface Removal of Chlorophenols from Water using Polyvinyl Pyrrolidone (PVP) Discussion on the Uncertainties in Hydrological Modeling Refractory Organic Matter in Antarctic Seawater Hydrodynamic and Mass Transfer in Wiped Film Evaporator Marine Microbial Processes and Methodology (Frontiers and Technological Advances in Microbial Processes and Carbon Cycling in the Ocean) Mercury Behavior in the Water Column of an Impacted Coastal Lagoon: Ria de Aveiro (Portugal) as a Case Study Approaches to Estimating the Depth and Duration of Playa Lake Flooding Resolving Meniscus Movement within Rough Confining Surfaces via the Level Set Method The Hydrological Role of Forest in Siberia Municipal Wastewater Treatment and Antibiotic Resistance -- A Case Study Recent Tendencies in the Synthesis of Pillared Clays for Phenol Oxidation Application of the Natural Flow Regime Concept to the Development of A Comprehensive Streamflow Analysis Approach. Example of Annual Maximum Spring Flood in Southern Quebec Vertical Hydraulic Conductivity of Highly Permeable Alluvial Aquifers Detection of Adenoviruses and Hepatitis A Virus in Water Samples and Oysters: Use of Three Different Nucleic Acid Extraction Methods Modeling Rainfall-Runoff Process and Identifying System Response Function using Multi-Resolution Analysis Index.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.021 |
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".