Bibliographic record
Abstract
A growing awareness of the value provided by Alberta's watersheds acts as a necessary first step for incorporating their true value into land-use decision making.Watersheds function to move nutrients through ecosystems, regulate and cycle water, absorb heat, and transfer energy.These "goods and services" from the ecosystem produce clean water, timber, and recreational opportunities. 5Specific watershed features, such as forests, riparian areas, or wetlands, are very significant for maintaining water quality as an ecosystem service and water supply as an ecosystem good.Alberta's Eastern Slopes of the Rocky Mountains, for example, are mainly covered by forest and are the source of water for a number of downstream needs including agricultural, municipal, and aquatic ecosystem needs.These watershed functions can be formally recognized.As Voora and Venema state in their ecosystem service study of the Lake Winnipeg watershed, "ecosystem service losses or gains provide an economic rationale for the preservation and restoration of environmental assets" as well as allow for more objective analysis of trade-offs in land-use decision making.Quantifying the value of ecosystem goods and services, while not able to fully account for the intrinsic and cultural value of the ecosystem, provides a means to communicate the importance of ecosystems to human well being. 6e ecological goods and services offered by key landscape features, such as forests, wetlands, riparian areas, and groundwater recharge zones, are addressed in turn below.Following each section is a discussion of monetary values that have actually been assigned to these features where they are available.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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; both teacher heads agree on what is shown here.
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".