Frost-heaving damage to one-year-old <i>Picea abies</i> seedlings increases with soil horizon depth and canopy gap size
Bibliographic record
Abstract
We studied first winter frost-heaving damage to one-year-old Picea abies (L.) Karst. seedlings planted in gaps made by group fellings (large circular gaps, ca. 500 m2) and single-tree selection cuttings (small irregularly shaped gaps, ca. 175 m2), as well as in uncut forest. One-month-old seedlings were planted on manually exposed LF, Ae, and B horizons that emulated various intensities and depths of scarification. The three experimental sites were located in multistoried Pinus sylvestris L. or P. abies forests on sandy loam or silt loam in southeastern Norway. Altogether, 5% of seedlings sustained frost heaving damage on the LF horizon, compared with 20% on the Ae horizon and 45% on the B horizon. On average, 31% of the seedlings in large gaps incurred frost-heaving damage compared with 20% in small gaps and 19% in uncut forest. Exposed roots and poorly anchored or uplifted seedlings were recurring classes of damage, especially on the B horizon and in large gaps. The above- versus below-ground biomass ratio of seedlings was higher on the B than on the Ae horizon in uncut forest and large gaps, inferring broken roots. Therefore, to reduce the risk of frost-heaving damage, shallow soil preparation and smaller gap sizes should be used.
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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 | 0.000 | 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) | 0.001 | 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".