Bibliographic record
Abstract
Wetlands are among the most ecologically productive lands in the world, but every year they continue to be lost due to increasing pressures from agriculture, industrial development, urbanization and the lack of effective mitigation to deal with such pressures.Despite environmental assessment processes, policies, and regulations to ensure the mitigation of affected wetlands, wetlands continue to experience a loss in areal extent, but more importantly, a functional net-loss.This is attributed, in large part, to the lack of incorporating cumulative effects principles into project-based wetland impact assessment and mitigation.The majority of activities that affect wetlands are either assessed at the screening level, where cumulative effects are rarely considered, or are deemed insignificant and do not trigger any formal environmental assessment process.As a result, the mitigation of cumulative effects on wetlands is often insufficient or completely lacking in development planning and decision-making.Part of the challenge is that there currently does not exist methodological guidance as to how to identify wetland cumulative effects and corresponding mitigation needs early in the project design process.This research presents a methodological framework and guidance for the integration of cumulative effects in decision-making for project-based, wetland impact mitigation.The framework provides a means for the early indication, assessment, and mitigation of the potential cumulative effects of project developments on the wetland environment, with the objective of ensuring a no-net-loss of wetland functions. * Highlighted cells indicate the months in which imagery was acquired '--' indicates no data available* Where criterion i (row) is significantly different than criterion j (column) as expressed by: > = criterion i significantly greater than j < = criterion i significantly less than j / = cannot be said that criterion i and j are different
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".