Bibliographic record
Abstract
of 54 EU influence in environmental policies in 75, 79, 118, 121 fish stock of 120 eutrophication in 54, 67 nutrient pollution in 69 strategic value of 56, 67, 74, 80 Baltic Sea Action Summit (2010) 72 Baltic Summit (2013) 62-3, 71, 75 political use by Russian Federation 75 Barents Sea 89, 102, 104, 106, 109-13, 120, 136 cod quotas of 92, 94 delimitation agreement (2010) 128 fish stock of 4, 86, 99 overfishing in 88 Bedritsky, Alexander 32-3 head of Roshydromet 27-8, 36 biodiversity 65, 67, 75, 119 loss of 54 Bogdanov, Nikolay 104 Brazil Rio de Janeiro 10 Canada 26, 48, 109, 112 withdrawal from Kyoto Protocol 45 carbon dioxide (CO 2 ) absorption of 2 emissions 2, 32 carbon market 32, 35, 45, 130 establishment of 36 Russian 43, 45 carbon sinks 12, 24, 44 forest 25-6, 44 Chechnya 20 China 30, 32, 48, 133 Christianity Orthodox 18 Chubais, Anatoly head of RAO UES Rossii 29 climate change 23, 34, 44 mitigation 119, 138-9 climate policy 3, 28-9, 40, 49, 73, 79, 130 international 43 Russian 46 Cold War 80 Commonwealth of Independent States (CIS) 71 Convention of the Continental Shelf (1958) 89, 106 cooperation discourses 79 benefits discourse 73-4 environmental discourse 73, 77-9 great power discourse 73, 75-6 partnership discourse 73, 76-7 Council of the Baltic Sea States (CBSS) 71, 75, 118 Darst, Robert 2, 73 Denmark 58, 61, 88 government of 62 Development Strategy of Maritime Activities of the Russian Federation to 2030 (2010) 71 Dvorkovich, Arkady 32 Ecodefense recipient of Swedish Baltic Sea Water
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.033 |
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; both teacher heads 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".