Bibliographic record
Abstract
Pesticide-Related Illness Reported to and Diagnosed in Primary Care: Implications for Surveillance of Environmental Causes of Ill-Health Heavy Metal Tolerance in Stenotrophomonas maltophilia Evaluation of Protein Pattern Changes in Roots and Leaves of Zea mays Plants in Response to Nitrate Availability by Two-Dimensional Gel Electrophoresis Analysis Contamination of Rural Surface and Ground Water by Endosulfan in Farming Areas of the Western Cape, South Africa Microbial Contamination and Chemical Toxicity of the Rio Grande Increased Litterfall in Tropical Forests Boosts the Transfer of Soil CO2 to the Atmosphere Explaining the Evolution of Warning Coloration: Secreted Secondary Defence Chemicals May Facilitate the Evolution of Visual Aposematic Signals Marine Biofilm Bacteria Evade Eukaryotic Predation by Targeted Chemical Defense Assessing the Distribution of Volatile Organic Compounds Using Land Use Regression in Sarnia, Chemical Valley, Ontario, Canada Chemical and Physical Properties of Some Saline Lakes in Alberta and Saskatchewan Chemically Diverse Toxicants Converge on Fyn and c-Cbl to Disrupt Precursor Cell Function Reverse and Conventional Chemical Ecology Approaches for the Development of Oviposition Attractants for Culex Mosquitoes Estrogen-Like Activity of Seafood Related to Environmental Chemical Contaminants The Environmental Toxicant 2,3,7,8-Tetrachlorodibenzo-P-Dioxin Disrupts Morphogenesis of the Rat Pre-Implantation Embryo
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.231 | 0.149 |
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".