Towards a European classification of forest humus forms
Bibliographic record
Abstract
The Canadian (Green et al. 1993) and French (Brêthes et al. 1998) classification systems \nare frequently used in an international context, but don’t cover all site conditions of \nEuropean forest ecosystems. Throughout the last decade, new national classification \nsystems were developed in Austria, Germany, and the Netherlands. \nBasic concepts of most national European classification systems are similar along general \nlines. Nevertheless there are differences in parameters used for description and classification of humus forms as well as in scaling these parameters. This results in incompatibility of classifications on the lower levels of the systems. So, i.e. regional humus forms cannot be described and compared as similar designations of humus forms often having differing contents, and similar contents having differing names. \nThe present paper gives a general outline of a concept on a classification system of humus forms at the European level. As a first step, the classification is outlined for terrestrial (aerobic) humus forms.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".