Introducing\nthe Adverse Ecosystem Service Pathway\nas a Tool in Ecological Risk Assessment
Bibliographic record
Abstract
Soils\nprovide numerous ecosystem services (ESs) such as food production\nand water purification. These ESs result from soil organism interactions\nand activities, which are supported by the soil physicochemical properties.\nRisk assessment for this complex system requires understanding the\nrelationships among its components, both in the presence and absence\nof stressors. To better understand the soil ecosystem and how exposure\nto potentially toxic elements impact ESs, we developed a quantitative\ntechnique, the adverse ecosystem service pathway (AESP) model. We\nsampled 47 soils across Canada and analyzed them for properties that\nincluded pH and cation exchange capacity. We spiked the soils with\na metal mixture and measured 15 soil processes representing five ESs.\nUsing a Pearson correlation, we confirmed that proxies of ESs are\nlinked to soil properties. <i>t</i> test results showed\nthat, apart from soil enzyme activities (<i>p</i> > 0.05),\nthe processes underlying ES proxies are significantly reduced in metal-impacted\nsoils. Using soil properties as predictors of ES proxies, we developed\nAESP models: one for spiked and another for control soils. These models\nshowed adverse effects on ESs in spiked soils, depicted as changes\nin partial correlation coefficients. The AESP model, therefore, can\nbe an important tool to understand complex ecosystems and improve\nrisk assessment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.462 | 0.008 |
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".