Ecologically Safe Architecture of Agrolandscape Is Basis for Sustainable Development
Bibliographic record
Abstract
Worldwide, about 12x106 ha of arable land are destroyed and abandoned annually due to soil erosion. This problem is very serious for Krasnogvardeiskiy district, which is most eroded district (72.7% eroded lands) in the Central Chernozem Zone (CCZ) of Russia. Under the leadership of the academician of Russian Academy of Agrarian Sciences O.G. Kotlyarova the landscape agricultural systems (LASs) were mastered throughout the Krasnogvardeisky district (132 thousand ha). Our investigations showed that the purpose for which LASs were created is reached. Obtained results showed that it is possible to prevent erosive losses and to raise soil fertility. Restoration of soil fertility is presented in terms of increasing of crops productivity-on 25%. Transformation of a landscape basis has increased the general biological capacity of territory more than twice and exchangeable biological capacity by 32%. All these facts have raised assimilability of the transformed territory that allows to consider it, unlike traditional, not as source ??2 in atmosphere, and as fixed carbon tank which capacity can reach 0,71 t/ha. In turn the intensification of substances circulation leads to increasing of plant production efficiency. Excess is from 14% (productivity of labour expenses) to 43.2% (use efficiency of PAR). In intensively mastered agricultural district (arable lands more than 80%) agroecosystems have been formed, allowing besides its basic function: soil preservation, steady reception of agricultural production, to carry out also the major biospheric function - increasing biological variety, creation of a favorable inhabitancy for live organisms, including human.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".