The influence of agricultural land-use on plant and macroinvertebrate communities in springs
Bibliographic record
Abstract
Many freshwater cool springs can be characterized by nearly constant temperatures and chemical composition. Agricultural activities may leach nutrients to the groundwater, add sediment and nutrients from overland flow, and change the cover of the riparian area surrounding springs, all factors that influence aquatic invertebrate and plant communities. Twenty limnocrene springs in Prince Edward Island, Canada (10 surrounded by and within 20 m of agricultural land, and 10 located in forested areas with < 5% agriculture within 1 km) were studied to determine effects of agricultural activities on invertebrate and plant community structure. Chemical, flow, sediment, and cover variables were examined in all 20 springs, and invertebrates and macrophytes were evaluated in a subset of four agricultural and four forested springs. Although nutrients (particularly nitrate) were higher in agricultural springs than in forested ones, and plant communities differed between springs in the two land use types, light level (relating to the riparian canopy in the two land-use types) was a stronger predictor of aquatic plant community composition than nutrients. Plant diversity was highest in open agricultural sites. Overall invertebrate richness and abundance were higher in forested sites than agricultural sites, but invertebrate community composition differed between the two land-use types, and was primarily related to aquatic plant composition. Few taxa responded directly to elevated nutrients. The composition of the riparian area may be more important than direct inputs of nutrients and sedimentation when assessing agricultural impacts on springs.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".