Database and report on currently already existing or announced ocean NETs projects, including a world map of projects
Bibliographic record
Abstract
Emissions trading systems (ETS) and markets usually do not allow for the inclusion of carbon dioxide removal (CDR) activities and if they do, removal activities are primarily restricted to afforestation. The New Zealand emission trading system (NZ ETS), for examples, integrates afforestation, and the California Low-Fuel Standard, the Quebec ETS and the Chinese ETS permit the restricted inclusion of afforestation offsets. Furthermore, the California Low-Carbon Fuel Standard System allows for the inclusion of removal via Direct Air Capture. In combination with the 45Q tax credit program, the largest incentives for CDR via Negative Emissions Technologies (NETs) are currently provided in the US. However, both do not yet allow for the inclusion of ocean-based carbon removal. Hence, we provide first a brief overview about the NZ ETS and its inclusion of afforestation, pointing out that the concept will likely not be applicable to ocean-based CDR with the potential exemption of blue carbon projects. Second, we discuss the 45Q tax credit program, the California Low-Fuel Standard System, and the California Compliance Offset Scheme. Third, we provide an overview about the company database related to ocean-based carbon removal. Fourth, we briefly look at the voluntary carbon market, providing some insights for carbon removal accounting.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.154 | 0.103 |
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".