Prescriptive and empirical principles of applied ecology
Bibliographic record
Abstract
Applied ecology is the science of managing ecosystems for defined outcomes, such as conservation, sustainable harvest, and animal pest and weed control. Robust knowledge in science is often expressed as “principles”. Principles in applied ecology have utility by assisting scientists and managers to evaluate current management and to plan future activities. Principles also have a unifying role by identifying general patterns and processes across a broad discipline. We review usage of the word principle in applied ecology by critically evaluating principles proposed previously. We identify and describe two principal uses of principles; first, a prescriptive principle defined here as a general guideline for applied ecological research and management, and second, an empirical principle defined here as a broad generalization based on replicated empirical observations and experiments. Principles proposed previously are invariably for particular applications and are not generic across applied ecology. The principles are consolidated here in a new set of 22 prescriptive and 3 empirical principles. The new principles are more comprehensive than those proposed previously and relate to all aspects of applied ecology, extending across conservation, sustainable utilization, and management of animal pests and weeds. The principles should assist applied ecologists and managers to achieve specific management objectives.
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 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) | 0.000 | 0.001 |
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".