Phosphorus Interactions with Iron in Undisturbed and Disturbed Arctic Tundra Ecosystems
Bibliographic record
Abstract
Phosphorus (P) limitation often constrains biological processes in Arctic tundra ecosystems. Although adsorption to soil minerals may limit P bioavailability and export from soils into aquatic systems, the contribution of mineral phases to P retention in Arctic tundra is poorly understood. Our objective was to use X-ray absorption spectroscopy to characterize P speciation and associations with soil minerals along hillslope toposequences and in undisturbed and disturbed low-lying wet sedge tundra on the North Slope, AK. Biogenic mats comprised of short-range ordered iron (Fe) oxyhydroxides were prevalent in undisturbed wet sedge meadows. Upland soils and pond sediments impacted by gravel mining or thermokarst lacked biogenic Fe mats and were comparatively iron poor. Phosphorus was primarily contained in organic compounds in hillslope soils but associated with Fe(III) oxyhydroxides in undisturbed wet sedge meadows and calcium (Ca) in disturbed pond sediments. We infer that phosphate mobilized through organic decomposition binds to Fe(III) oxyhydroxides in wet sedge, but these associations are disrupted by physical disturbance that removes Fe mats. Increasing disturbances of the Arctic tundra may continue to alter the mineralogical composition of soils at terrestrial-aquatic interfaces and binding mechanisms that could inhibit or promote transport of bioavailable P from soils to aquatic ecosystems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".