Wetland Restoration Using Mangroves in Southern Louisiana
Bibliographic record
Abstract
Abstract Wetland restoration is essential in addressing wetland loss along the coasts of Southern Louisiana. Wetland restoration techniques, such as planting mangroves, provide a wealth of benefits such as storm surge reduction, fish and wildlife habitat, carbon sequestration, recreation, job creation, and economic development that are vital to the sustainability of coastal Louisiana. The Louisiana Land & Exploration Company, a wholly owned subsidiary of ConocoPhillips, is one of the largest private owners of coastal marsh in the nation, owning approximately 640,000 acres located in the coastal zone of southeast Louisiana. A pilot project was recently initiated by ConocoPhillips and Tierra Resources to apply the latest scientific approaches to measure the benefits of mangrove plantings for restoration purposes and viability of carbon sequestration on ConocoPhillips’ property in Southern Louisiana. This paper discusses the pilot project as well as the application of the first certified methodology for quantifying the carbon sequestration benefits of this wetland restoration project. Results from this three-year (2012–2015) pilot project will address science gaps, determine costs, benefits, and barriers to implementation.
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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 | 0.000 |
| 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 | 0.001 | 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 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".