Effects of soil treatments and tree species on reforestation of well pads
Bibliographic record
Abstract
Anthropogenically induced arrested succession is a global problem in forest ecosystems. In northern Michigan (U.S.A.), oil development has arrested succession in forest landscapes since the 1970s and oil well pads have not reforested up to 30 years after being vacated. To explore pathways for reforestation, we evaluated survivorship and growth of northern red oak (Quercus rubra), white oak (Q. alba), jack pine (Pinus banksiana), and red pine (P. resinosa) at monthly intervals during growing seasons from 2015 to 2019 on pads, which had been vacated 9–34 years prior to the initiation of our study. We planted trees in four soil surface treatments—experimental control (planting only, no treatment), disking, fertilization, and disking with fertilization. While monitoring survivorship and growth of planted trees, we concurrently examined natural reforestation in untreated control areas on each pad and differences between soil characteristics of pads and surrounding forests. Pinus resinosa displayed highest overall survivorship regardless of treatment, but maximum survivorship in disked soils and experimental control areas. Other species had highest survivorship in disked soils. There was no significant reforestation in untreated control areas during the 5‐year study period. Pads had higher levels of Bray P, Ca, and Mg, but lower levels of Fe, organic matter, and moisture than forest soils. Concentrations of toxic chemicals associated with drilling activities were not different between pad and forest soils or, if different, within acceptable levels for plant growth. In reforesting well pads, restorationists should plant P. resinosa in disked soil to achieve highest tree survivorship and density.
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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) | 0.001 | 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) | 0.001 | 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".