Bibliographic record
Abstract
Acid soil extractants to assess metal phytoavailability, 253 Acidic and calcareous soils forms of compost phosphorus, 711 Actinorhiza biochar enhances seedling growth, 329 Adventitious roots organic layer depth in forests, 799 Aggregate-associated carbon fertilizer treatments on waterstable aggregates, 551 Aggregate size distribution cattle manure on soil aggregate size and nutrients, 673 moisture and freeze/thaw on aggregate size, 529 Aggregate stability long-term grazing effects on soil properties, 685 moisture and freeze/thaw on aggregate size, 529 Agriculture agriculture's effects on the classification of Black soils, 403 Agromine´raux influence des agromine´raux sur la production du soja, 905 Alnus viridis ssp.sinuata biochar enhances seedling growth, 329 Amelioration reclamation of lignite mining areas, 53 Amendment, soil diamond mine reclamation in the Canadian north, 77 subsoiling and pellet injection on soil quality, 269 Ammonium retention and nitrification of injected ammonia, 589 Amylase screening bioassay for soil PHC toxicity, 901 Anthroposolic Order, 7 Are´nosols roches vertes de Gangila comme amendement des sols sableux, 787 Argillic horizon freeze-thaw and soil erosion in northeast China, 567 Asbestos reclamation of asbestos, 229
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.880 | 0.831 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".