Bibliographic record
Abstract
Absorption Spectroscopy) 103 Ablation 304 Advanced tracers 57 AES (Atom Emission Spectroscopy) 103 Aerial photos 247 Accident 8, 10, 252 accidental leaks, spills 248, 252 Acid Red 60 Acid Yellow 60 Acid Blue 9 see Brilliant Blue Acid dissociation constant 115 Acidic media, acidic environments 72, 83 Actinometer (fluorescence actionmeter) 75 Activatable tracers 58 Active charcoal 100-2, 310 Adiabatic cooling 26, 28-9, 29 Advection-dispersion model 253 Age dating Argon-39 50 Carbon-14 13, 51 Krypton-85 13, 50, 51 CFCs, see Chlorofluorocarbons 13 SF 6 13 Tritium 43 Alberta, Canada 247 Alert model 254-7, 273 Aletsch glacier 307-8 Alpine glaciers experiments 305, 306, 310-21 Amidorhodamine G 60, 67, 73-4, 74, 76, 82-3, 86, 87, 90, 91, 185, 192, 312 experiment in glacier 239-46 Ammonium 42-3, 121, 293 Ammonium carbonate 121 Amphiphilic 76 Analysis active charcoal 101 FBA tracers 117 fluorescent tracers 89-95 gas tracers 119-120 isotopes 13-6 lycopodium spores 109 microspheres (fluorescent) 111 Andarax catchment 53-6B Anion 76, 102-104, 106-7, 114-7, 114, 214, 336 exclusion 76, 104 Anthraquinones 60, 66 Aperture see Fissure aperture Apparent parameter 164 Aquaeous diffusion coefficient see (molecular) Diffusion coefficient Aquatic ecosystem 246 Aquiclude 105 Aquifer 1, 3-6, 10, 30, 39, 42
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 | 0.001 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.833 | 0.765 |
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".