A hierarchical analysis of ecosystem classification with implementing in two continental ecoregions
Bibliographic record
Abstract
Abstract Background The ecosystem classification of land (ECL) has been studied for a couple of decades, from the beginning of the perfect organism system “top-down” approach to a reversed “bottom-up” approach by defining a micro-ecological unit. After comparing two cases of the ecosystem classification framework implemented in the different continental ecoregions, the processes were carefully examined and justified. Results Theoretically, Bailey’s upper levels of ECL (Description of the ecoregions of the United States, 2nd ed. Rev and expanded (1st ed. 1980). Misc. Publ. No. 1391 (Rev). Washington DC USDA Forest Service; 1995) were applied to the United States and world continents. For the first time, a complete ECL study was accomplished in Western Utah of the United States, with eight upper levels of ECOMAP (National hierarchical framework of ecological units. U.S. Department of Agriculture, Forest Service, Washington, DC. https://www.researchgate.net/publication/237419014_National_hierarchical_framework_of_ecological_units ; 1993) plus additional ecological site and vegetation stand. China’s Eco-geographic classification was most likely fitted into Bailey’s Ecosystem Classification upper-level regime. With a binary decision tree analysis, it had been validated that the Domains have an empty entity for 500 Plateau Domain between the US and China ecoregion framework. Implementing lower levels of ECL to Qinghai Province of China, based on the biogeoclimatic condition, vegetation distribution, landform, and plant species feature, it had classified the Section HIIC1 into two Subsections (labeled as i , ii ), and delineated iia of QiLian Mountain East Alpine Shrub and Alpine Tundra Ecozone into iia-1 and iia-2 Subzones. Coordinately, an Ecological Site was completed at the bottom level. Conclusions (1) It was more experimental processing by implementing a full ECL in the Western Utah of the United States based on the ECOMAP (1993). (2) The empty entity, named as Plateau Domain 500, should be added into the top-level Bailey’s ecoregion framework. Coordinately, it includes the Divisions of HI and HII and the Provinces of humid, sub-humid, semiarid, and arid for China's Eco-Geographic region. (3) Implementing a full ECL in a different continent and integrating the lower level's models was the process that could handle the execution management, interpreting the relationship of ecosystem, dataset conversion, and error correction.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".