Effect on disturbance type (fire and harvesting) on the ecological diversity of carabid beetles (Coleoptera: Carabidae) in black spruce (Picea mariana (Mill.) BSP.) forests of eastern Manitoba
Bibliographic record
Abstract
Disturbance, especially fire, is a crucial part of boreal forest succession, returning the forest to return to an early successional stage. In the boreal forest, fire had been the main disturbance type on the landscape until advances in mechanical harvesting methods allowed for large scale harvesting. At the same time as these advances in harvesting, there has been an increase in fire suppression by active firefighting (Smith et al. 2000). Large-scale harvests are becoming the main disturbance type on the landscape, causing fire-initiated succession to become a threatened process (Kimmins 1997, Niemela 1999). With harvesting now becoming a significant disturbance, forest health must be quantified to determine whether harvesting has the same impact on the forest as fires. Forest health can be assessed using bioindicators. Bioindicators are a group of organisms (that can be from various taxonomic levels) used to represent the diversity patterns of all other organisms in an ecosystem (National Research Council 2000). Choosing the most suitable indicator is based on a combination of the following three criteria: how representative the indicator is of the ecosystem, ease of identification of the indicator, and the time and cost of sampling (Anderson 1999). Carabid beetles (Coleoptera: Carabidae) ecophysiological adaptations (Thiele 1977) and their ability to cope with disturbances in forests make them ideal bioindicators of forest health (Rainio and Niemela 2003). Carabid fauna in several parts of the world, has been investigated in forested systems, including Poland (Fedorenko 1999), Finland (Niemela et al. 1994b), the United Kingdom (Jukes et al.2001), and Canada (Holliday 1991, Niemela et al. 1993)...
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".