Using land cover information in assessing the ecosystem health of streams
Bibliographic record
Abstract
Abstract Context Land use in a catchment area is critical to understanding how human activities are impacting streams. Catchment land cover is typically quantified as proportions of land use types, but such proportions do not quantify where land use patches are relative to the stream. Objectives This paper discusses the merit of land use position metrics for application to stream assessments. Methods and results Landscape configuration metrics (LCMs) are often used in stream assessments to describe land use position, but we argue these metrics should be avoided due to: (1) poor description of catchment land cover; (2) inconsistency, and; (3) missing link between valley and stream. Inverse-distance-weighted metrics (IDWs) explicitly quantify the position of land use patches relative to the stream, and thus are conceptually grounded in empirical evidence that the effect of land use is inversely related to its distance from the stream. Hydrologically active IDWs (HA-IDWs) further refine IDWs by quantifying the proximity of land use to hydrologic pathways connecting a stream to its catchment. Conclusions We recommend IDW metrics become the standard method to describe catchment land use and its effect on stream conditions and that HA-IDW metrics be used when the required data is available.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".