Bibliographic record
Abstract
Human activities potentially threaten key ecological processes, or "ecosystem functions", mainly through the habitat conversion associated with urbanization and agriculture. Although ecosystem functions can clearly be disrupted in severely degraded systems, it is not clear how those functions vary along the entire gradient of human activity at scales most relevant to global environmental change. To address this question, I used two remotely sensed indices of ecosystem function, as measured by the normalized difference vegetation index (NDVI) and thermal infrared radiation (TIR), to derive estimates of primary productivity and evapotranspiration, respectively, at 1-km resolution across multiple vegetation types in southern Canada. After controlling for the variation in NDVI and TIR related to the climatic gradient, I related these indices to measures of anthropogenic activity (road density, extent of natural cover, and protected areas status). While NDVI and TIR are both strongly related to climate and vegetation type, much of the residual variation in NDVI (up to 67%) and TIR (up to 55%) is related to human activity. Ecosystems in areas of intense human impact are generally less productive and exhibit less water cycling (i.e., energy-transforming) efficiency, but I found no evidence of threshold effects in the response of ecosystem function to increasing human impact. Ecosystems in protected areas (parks and reserves) have significantly higher productivity and, to a lesser extent, higher evapotranspiration, which suggests increased solar energy-transforming capacity. These relationships are strongest at coarse spatial scales and are generally consistent within different vegetation types. The magnitude of these effects along the entire gradient of human activity is substantial.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".