Модели роста и взаимодействия деревьев
Bibliographic record
Abstract
1. Федеральное агентство лесного хозяйства, международный институт прикладного системного анализа. Таблицы и модели хода роста и продуктивности насаждений основных лесообразующих пород Северной Евразии (нормативно-справочные материалы). М., 2008. 886 с. 2. Колобов А. Н., Фрисман Е. Я. Моделирование процессов динамической самоорганизации в пространственно распределенных растительных сообществах // Математическая биология и биоинформатика. 2008, Т. 3, вып. 2. С. 85–102. 3. Shaw C., Bhatti J., Chertov O., Nadporozhskaya M., Komarov A., Bykhovets S., Mikhailov A., Apps M. Application of the forest ecosystem model EFIMOD 2 to jack pine along the boreal forest transect case study // Canadian Journal of Soil Science. 2006. Vol. 86. No. 2. P. 171–185. 4. Карев Г. П., Скоморовский Ю. И. Моделирование динамики однопородных древостоев// Сибирский экологический журнал. 1999. Вып. 4. С. 403–417. 5. Колобов А. Н., Фрисман Е. Я. Моделирование процесса конкуренции за свет в одновозрастных древостоях // Изв. РАН. Серия биологическая. 2013. № 4. С. 463–473. 6. Заварзин Г. А. Становление биосферы // Вестн. РАН. 2001. Т. 71, вып. 11. С. 988–1001. 7. Гурцев А. И., Цельникер Ю. Л. Фрактальная структура ветви дерева // Сибирский экологический журнал. 1999. Вып. 4. С. 431–441. 8. Большакова Н. В. Влияние густоты и размещения посадочных мест на рост ели при выращивании культур по интенсивным технологиям: Автореф. дис. … канд. сельск.-хоз. наук. СПб., 2007. 20 с. URL: http://earthpapers.net/preview/53938/a?#?page=1
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".