“Land use is reliably predicted by soil burst respiration profiles”
Bibliographic record
Abstract
Studies examining soil respiration across land uses have been published for over 100 years, but few studies have examined how variability in rates of respiration during a test period could elucidate microbial responses to wet-dry events and connect them to land use. Our study examined the interplay between land uses (LU) and maximum potential soil respiration across Atlantic Canada using partitioned profiles of ‘burst’ respiration measurements. We found that Crop Agriculture respiration peaked in the first 24 h of the incubation, while Forest peaked during the second and third 24 h periods. Pasture respiration was intermediary, peaking in the first two 24 h periods. These specific proportional respiration intervals were crucial in successfully predicting Crop Agriculture, Forest and Pasture LUs using machine learning models, with accuracy equivalent to comprehensive soil physicochemical parameters. Wetland respiration profiles were inconsistent with minimal predictive power, likely due to high variability of mineral and carbon (C) content. While early (24 h) respiration was proportionally largest for managed soils, indicating the impact of management in increasing respiration, absolute total respiration over 72 h was largest for natural soils, indicating their likely larger putative contribution, per unit area, to the atmospheric C pool. This study points to a novel approach for assessing the effect of agricultural and natural management on soils, that recognizes consistent gradients in the functional status of soil C as directly linked to land use and land management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".