Evaluating ecological outcomes from environmental stewardship initiatives: A comparative analysis of approaches
Bibliographic record
Abstract
Understanding the extent to which stewardship initiatives achieve objectives of enhanced ecological outcomes is important for enhancing effectiveness and efficiency of environmental management initiatives. Alternative approaches - community science, stakeholder perceptions, and remote sensing - are emerging in lieu of the conventional approach of collecting field data that present different benefits and drawbacks and to date have not been directly compared. This research compared the use of four approaches to evaluating ecological outcomes of a grassland restoration project on a 2 ha Niagara Parks Commission property in Ontario, Canada. We collected three levels of quantitative data, from general site assessments to species-specific data using standardized questionnaires and multi-spectral imagery from a remotely piloted aircraft system. We found that community scientists and stakeholders provided comparable general site assessments to the field data, but that as the assessments became more detailed, differences emerged. Further, remotely sensed data were assessed and provided a more positive site assessment than any other method. Experiences and knowledge of nature did not influence assessments by community scientists or stakeholders. Our findings show that for overall site assessments, community scientists and stakeholders may be able to provide a reasonably accurate assessment. If monitoring and evaluation needs (either research-based or practical) extend beyond a broad assessment, use of a field expert or multiple methods of data collection may be warranted.
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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 | 0.058 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".