Automating the process of net environmental benefit analysis (NEBA) for emergency response and environmental plans
Bibliographic record
Abstract
Each year in Australia, hundreds of referrals and environmental plans (EPs) are submitted to the regulatory agencies by oil and gas explorers. The plans assess multiple spill response options. One assessment tool is NEBA. This extended abstract presents a new approach for NEBAs and an automated NEBA process. This NEBA method has two scenarios: pre-spill and spill response. The pre-spill NEBA uses EP-defined sensitive receptors as appropriate examples in the region of interest, uses the modelling outputs to identify potential exposure zones, defines critical inputs (season, spill size), defines and ranks priority response according to the modelling output, and assesses spill response strategies. The output of the pre-spill NEBA is a table of response tactics to be assessed with respect to reducing risk to as low as reasonably practicable (ALARP). The spill response NEBA is similar to the pre-spill, but it uses real-time spill modelling data as an additional input to strengthen the data evaluation. Traditionally, both pre- and spill response NEBAs require considerable data input, extensive mathematical modelling and human interpretation. Automating the NEBA process saves time, reduces errors and minimises human biases in interpretation. ConocoPhillips has recently developed a tool to quickly conduct NEBAs. The NEBA tool also provides a means to document the progress of operational and scientific monitoring programs to measure the performance of the spill response. The automated process output can be time/date stamped to ensure critical steps are fully documented and auditable.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".