Effects of harvesting and soil disturbance on soil CO<sub>2</sub> efflux from a jack pine forest
Bibliographic record
Abstract
We measured organic matter removal and soil compaction effects on soil surface CO2 efflux (F) from a jack pine (Pinus banksiana Lamb.) forest and developed an analytical framework involving multiplicative response functions to interpret response. Treatments included stem-only harvest (OM0C0), full-tree harvest (OM1C0), full-tree harvest with surface soil removal (OM2C0), full-tree harvest with surface soil removal and soil compaction (OM2C2), and uncut forest (UF). Mean F and calculated F at 10 °C under nonlimiting soil moisture conditions (F10) were greatest in treatments with intact organic surfaces and often larger in the OM2C0 than in the OM2C2. F10 showed strong linear relationships with detrital production in harvested plots, with total near-surface carbon in all plots, and was positively correlated with understory cover. F increased exponentially with soil temperature, with the most and least pronounced responses found in the UF and OM2C0 treatments, respectively. F also responded in parabolic fashion to relative soil water content. In the UF, F was often low in May because of cold soils, but subsequently attained rates equivalent to those of the OM0C0 and OM1C0, despite lower soil temperatures. Three to five growing seasons after treatment, soil temperature and moisture, together with F10, explained 71%87% of the plot-level variation in F.
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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) | 0.000 | 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".