SPATIAL MODEL FOR PREDICTING COMMUNITIES OF PREDATORY MITES (MESOSTIGMATA) OF LEAF LITTER
Bibliographic record
Abstract
The importance of the predatory mites Mesostigmata of the litter lies in its function of population regulation to maintain the balance of soil organisms.Leaf litter is one of the main habitats of these organisms, however, the effect of its properties on the spatial distribution of mites is still unknown.The objective of this research was to know the effect of physical and chemical properties of litter on the abundance of mites.Litter from coniferous, deciduous, and mixed forests was sampled, these forests were located 90 km northwest of Peace River, Alberta, Canada.The generalized additive model (GAM) with the negative binomial distribution modeled the overdispersion of the mite frequency, with an explained deviance of 71.8%.Second degree non-linear relationships were significant between the total abundance of Mesostigmata mites and the variables of Elevation, depth, temperature, humidity and pH.The relationship between abundance and geographic coordinates was fifth order, indicating that the greater abundance of mites was the result of geographic variability.The optimal conditions for the production of Mesostigmata mites are: Elevation below 700 meters above sea level, depth greater than 12 cm, temperature between 11 to 12 degrees centigrade and acidic pH.The least favorable percentage of humidity is between 60 to 90%.The number of mites in the coniferous forest (CD) was statistically equal to that in the deciduous forest (DD), but different from the mixed forest (MX) with p<0.05, the latter having a higher abundance of mites.It is concluded that GAM models are useful to estimate the abundance of mites and predict them in adjacent areas that have not been sampled.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".