Establishment of tree seedlings and water-soluble nutrients in coarse woody debris in an old-growth <i>Picea</i>-<i>Abies</i> forest in Hokkaido, northern Japan
Bibliographic record
Abstract
Forest floor microsite conditions and tree seedling establishment were studied at an old-growth Picea-Abies forest in Hokkaido Island, northern Japan. Tree seedlings were established abundantly on coarse woody debris (CWD) from decay class III, a class indicating moderate decay, to class V, the most advanced decay class. The height-class distribution of tree seedlings indicates that the recruitment of Picea glehnii (Fr. Schm.) Masters and Picea jezoensis (Sieb. et Zucc.) Carr. seedlings on CWD started on decay class II and was mostly restricted to CWD decay class III. Seedlings of Abies sachalinensis (Fr. Schm.) Masters also favored establishment on CWD but had a wide adaptability to most of the microsites. Although CWD functioned as a suitable seedbed, water extracts from CWD were acidic and had quite low mineral nutrient concentrations. Tree seedling establishment did not necessarily require high levels of nutrient content in microsites. Although the forest floor was largely covered by CWD, with 2056 m 2 ·ha -1 of the total projected area covered by CWD, CWD decay class III covered only 366 m 2 ·ha -1 of the forest floor, indicating that CWD as a functioning seedbed is limited by time and space on the forest floor.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".