Using Conceptual Models and Decision-Support Tools to Guide Ecosystem Restoration Planning and Adaptive Management: An Example from the Sacramento–San Joaquin Delta, California
Bibliographic record
Abstract
The Sacramento–San Joaquin Delta (the Delta) is located on the western edge of California’s Central Valley and is of critical ecological and economic importance. However, ecosystem alterations for human uses changed many of the Delta’s natural processes, and it is now considered in need of restoration. An approach was developed to evaluate and rank restoration actions in the Delta under the Ecosystem Restoration Program’s Delta Regional Ecosystem Restoration Implementation Plan (DRERIP). The DRERIP approach provides an explicit framework for evaluating restoration actions, using linked conceptual models, an action evaluation procedure, and a decision-support tool. Conceptual models allow scientists and managers to synthesize scientific information and make qualitative predictions about ecosystem function and restoration outcomes to guide and focus restoration efforts. The action evaluation procedure is a structured assessment of restoration actions. The procedure clearly describes actions to be evaluated, assesses the magnitude (importance and scale) and certainty of anticipated ecological outcomes, estimates degrees of worth (achieving intended outcomes) and risk (causing adverse consequences), evaluates the reversibility of the action, and identifies opportunities for learning. The values for worthiness, risk, reversibility, and learning opportunity are used in the decision- support tool to determine the fate of a proposed action. The decision-support tool is a structured decision tree that determines the disposition of an action: whether a restoration project should be discarded, revised with a different approach and re-evaluated, or implemented; and, if implemented, at what scale (targeted research, pilot project, or full implementation). The DRERIP approach provides managers with a valuable tool for restoration planning, and a foundation for integration with quantitative methods for a comprehensive ecosystem restoration plan.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".