Factors influencing patterns in distribution, abundance and diversity of sedimentary macrofauna in deep, muddy sediments of Placentia Bay, Newfoundland and the adjacent shelf
Bibliographic record
Abstract
The goal of this article is to understand strategies by which both the environmental and poverty alleviation objectives of PES programs can be achieved cost effectively. To meet this goal, we first create a conceptual framework to understand the implications of alternative targeting when policy makers have both environmental and poverty alleviation goals. We then use the Grain for Green program in China, the largest PES program in the developing world, as a case study. We also use a data set from a survey that we designed and implemented to evaluate the program. Using the data set we first evaluate what factors determined selection of program areas for the Grain for Green program. We then demonstrate the heterogeneity of parcels and households and examine the correlations across households and their parcels in terms of their potential environmental benefits, opportunity costs of participating, and the asset levels of households as an indicator of poverty. Finally, we compare five alternative targeting criteria and simulate their performance in terms of cost effectiveness in meeting both the environmental and poverty alleviation goals when given a fixed budget. Based on our simulations, we find that there is a substantial gain in the cost effectiveness of the program by targeting parcels based on the "gold standard," i.e., targeting parcels with low opportunity cost and high environmental benefit managed by poorer households.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".