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, defining micro-ecological unit. After reviewing two study cases of the ecosystem classification of land, the ecosystem classification framework implements in different ecoregions were examined and analyzed. Results: Theoretically, Bailey’s upper levels ECL (1995) was applied to the United States, and world continents. China's Eco-geographic classification was most likely fitted into Bailey’s Ecosystem Classification regime. With a binary decision tree analysis, it demonstrated that the top-level, Domain has an empty entity between the US and China ecoregion framework. Based on the biogeoclimate condition, vegetation distribution, landform, and plant species feature, classified 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 zone.Conclusions: 1) The Plateau Domain 500 should be added into the top-level Bailey’s ecoregion framework, coordinately it includes HI and HII Divisions, and humid, dub-humid, semiarid, and arid provinces. 2) Two case comparisons recommend using a practical approach, objectively defined ecosystem classification for the lower-level ECLs in matter of time and project cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".