Bibliographic record
Abstract
Chevron Canada Ellis River 100,000 767 KNOC BlackGold 20,000 153CNRL Birch Mountain 30,000 230 Laricina Germain 1,800 14CNRL Gregoire Lake 30,000 230 MEG Christina Lake 23,880 183CNRL Kirby 30,000 230 NAOSC (Statoil) Kai Kos Dehseh 140,000 1073CNRL Leismer 15,000 115 Nexen Long Lake 72,000 552CNRL Primrose/Wolf Lake 120,000 920 Nexen Long Lake South 70,000 537Connacher Great Divide 10,000 77 North Peace Energy Red Earth 1,000 8ConocoPhillips Surmont 25,000 192 Patch Ells River 10,000 77Devon Jackfish 35,000 268 Petrobank (Whitesands) 90,000 690EnCana Borealis 32,500 249 Petro-Canada Chard 40,000 307EnCana Christina Lake 30,000 230 Petro-Canada Meadow Creek 40,000 307EnCana Foster Creek 30,000 230 Petro-Canada Lewis 40,000 307Enerplus Kirby 25,000 192 Petro-Canada MacKay River 40,000 307Husky Caribou Lake 10,000 77 Shell (BlackRock) Orion (Hilda Lake)10,000 77Husky Sunrise 50,000 383 Shell Peace River 50,000 383Husky Tucker 30,000 230 Suncor Firebag 68,000 521Imperial Oil Cold Lake 30,000 230 Total (Deer Creek) Joslyn 15,000 115JACOS Hangingstone 25,000 192 Value Creation Terre de Grace 40,000 307
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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".