Oribatid mite (Acari:Oribatida) assemblage response to changes in litter depth and habitat type in a beech-maple forest in southwestern Quebec
Bibliographic record
Abstract
I investigated oribatid mite assemblages in a beech-maple forest in southwestern Quebec. I first examined the effects of four forest stand types (American Beech (Fagus grandifolia) dominated, Sugar Maple (Acer saccharum) dominated, mixed deciduous and coniferous plantations) and three open site types (agricultural field, fallow pasture and unmanaged hay field) in structuring oribatid mite assemblages. My second study focused on the effects of changes in litter depth (a factor that varies by stand type) on the structure of oribatid assemblages. Stand type was shown to be an important factor in determining oribatid mite abundances, species richness and assemblage composition. Results from the second study confirm this, but revealed no effect of changes in litter depth on oribatid mite assemblages. These findings serve to demonstrate that while examining specific environmental factors as determinants of oribatid mite diversity and distribution is important, more general factors such as habitat type cannot be ignored.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".